From Agile Scrums to Codeless Workshops - Tracking the evolution of enterprise software development

Will the transition from Agile Scrum development to Codeless Real-Time Development Workshops be as high impact as the move from Waterfall to Scrum?

The Journey from Waterfall to Codeless Software Development

In the Beginning

For companies that were accustomed to adopting a waterfall project approach for their software developments, the move to agile scrum methods has been dramatic, both in terms of its results and its impact on the size and skills of project teams.

Waterfall describes a traditional software development approach (that dates back well into the 1970’s) where different threads of development are performed by different development people or teams. 

When development teams adopt the waterfall method, projects are scope up front and then components are developed in parallel by different people – often employing specialist skills and tools.   The number of people involved in projects and the many threads of activity creates a complex project model that then requires expert project management tools, methods and skills to orchestrate (PRINCE2 became the answer to this eventually and led to a whole new set of skills requirements for companies to mince over).

Following development of the various streams of the waterfall, these are bound together and formed into a final ‘end-product’.  This approach has been fraught with project management issues. Delays in any of the parallel developments causes the entire project to over-run.  Often, ‘next steps’ of integration with third party tools and data, and activities such as testing and tuning, can’t be progressed until the last thread of development has been done. This can result in huge project over-runs and costs.  

The article ‘Why Your IT Project May Be Riskier Than You Think’ by HBR (November 2011), that followed a survey of 1,471 IT projects with an average spend of $167m, found that the average overrun was 27%, one in six of the projects studied was a black swan, with a cost overrun of 200%. And almost 70% of black swan projects also overrun their schedules.

Even when projects do get delivered on-time, the consequences of using one or many expert programmatic tools means that documentation is more complex, testing and tuning takes longer, and more bugs are likely to be embedded in the code. EVEN should everything go according to plan, the remoteness of intended system users and stakeholders to the back-room development process means that resulting systems are difficult to get right as a perfect fit to the user community.  It’s quite an art to write a paper specification for a software application and produce a perfect system right-first-time.  More like impossible in fact.

A major challenge all development teams face is application users and stakeholders rarely know PRECISELY what they need in terms of database management, reports, process workflows and user interfacing requirements out-of-the-box.  Normal domain expert business users find it hard to visualize end-applications and often struggle to formalize their end-product in a format that programmers can use. 

Accepting the high resourcing costs, need for costly specialist best-of-breed tools, use and constraints of coding and its impact on testing, tuning and adaptability, perhaps the biggest issue of traditional waterfall development tools and methods however lies in what gets left behind.  Programmed systems are inherently difficult to maintain and adapt. Companies don’t particularly want to be left with systems they can’t change over time, or that require a team similar in size to the team that built the application to maintain it into the future (mainly because of the dependencies created on a smorgasbord of best-of-breed tools and skills.

The step-up to AGILE SCRUM

Advances in Integrated Development Environment (IDE’s) since the turn of the century, aided by the timely emergence of standards around enterprise web server computing platforms, has produced a number of capable tool-kits to enable software development teams to work more closely together with fewer tools and fewer expert skills.  This reduction in tool-kit complexity has meant the project approach has been able to transition more towards a team-based culture where individuals working towards a shared goal can work together in a collegiate way.

AGILE tools are so-called because they provide development teams with more adaptive tools that produce results faster.  One aspect of the ‘agility’ comes from using bigger building blocks of code that can be shaped to fit a variety of needs but doesn’t require programmers to start with a blank page. 

The SCRUM model has evolved around the need for members of development teams to keep in close contact with one another and stay on the same page.  More frequent reviews of progress – perhaps on a weekly, even daily basis – ensures that the streams of development stay on-track and can be regularly check to ensure the quality of the end-product is as it needs to be and that project managers can check time and again that ‘what is being produced’ is ‘what the internal client wants’.

AGILE tooling does not provide developers with everything they need in one box.  To improve the integration and use of third party expert tools, software providers today normally provide an Applications Programming Interface (API) so the various tools are EASIER to integrate with one another.  While this is EASIER it remains a major overhead on developments as each API tends to be different and developments inherit any limitations presented by the ‘black box’ of functionality provided by the best-of-breed software they are integrating with.  This inability to interplay one application with another using the same properties means that developers find themselves constantly having to come up with work-arounds to produce the best end-product.

SCRUM has its limitations too. While it’s great for developer teams to work towards a common goal and take more of an active interest in making sure their contribution is producing the best ‘total’ outcome, developers continue to work in back-rooms and continue to lack an appreciation of the domain area they are building applications for.  Any developer knows the ‘last mile’ (i.e. managing the interaction with the user and stakeholder communities) is the most difficult part of an application to get right as different user groups will have very different expectations and needs from the applications they use. 

Like the traditional waterfall approach, AGILE SCRUM still relies on coding and that’s a problem.  Not only does it install more post–development overheads are more bugs are built into the end-product, and more testing, tuning, integration is necessary, it means that resulting applications continue to be expensive to maintain and adapt.

The New Age of Codeless Development

So-called ‘codeless’ development software describes Integrated Development Environments that displace the need for applications authors to code.  The leading enterprise platform for this approach is Encanvas.  All of the building blocks required to develop enterprise applications are built into a common IDE. ‘Design Elements’ - as they are known - adopt similar properties and have been designed to provide a sufficient level of tailoring to fit the majority of needs. 

At one time, it was thought that codeless platforms would be only good enough for piloting of projects or serve ‘little application’ needs of the enterprise where larger platforms were too costly to use.  The last decade has shown however that IDEs like Encanvas are capable of delivering enterprise, pan-regional and cross-industry applications to enterprise scale and levels of performance.

In the same way that a step-change in enterprise web platform standards at the turn of the century led to the evolution of SCRUM methods for software applications authoring, use of codeless tools has led to the formation of near-real-time applications authoring methods.

With codeless software, the removal of the programming activity in project (and the removal of best-of-breed tools and the need for APIs) has meant that users and stakeholders can be engaged in development workshops where applications designs are iterated in consort with the users and stakeholders who are able to see and understand what’s being authored because they see the ‘end-product’ being formed in-front of their eyes.

Codeless software has dramatically reduced the number of tools and the variety of skills needed to author applications, but its biggest impact is found in the leave-behind: resulting systems can be easily adapted as business requirements and user needs change.  Neither are there any upgrade costs associated with software; platforms like Encanvas enable hundreds of applications to be authored and do not charge for any new improvements to the IDE.  This means IT leaders seeking to reduce the number of technologies they support in their enterprise IT architecture can shrink their footprint from 100’s of tools to less than 10.

The bi-product of this change in technology tooling and methods makes a big impact on IT budgets. The expectation of IT leaders now is that IT operations should cost no more that 1% of operating profits. Deployments delivered through codeless workshop development methods demonstrate a factor of 10 increase in time-to-market and a similar dynamic in the lowering of onward IT costs.

Will the move from SCRUM to CODELESS be bigger or smaller than the move from WATERFALL to SCRUM?

I would argue the move from SCRUM to CODELESS has a greater bottom-line impact but a lesser change impact compared to the original transition from WATERFALL to SCRUM for the following reasons:

1. IT departments have already shrunk their internal IT teams and now use contractors for much of the heavy-lifting in software development and support. What the move to codeless does is internalize the support of IT systems – making IT more adaptive and better able to support the long-tail of demand for new or better enterprise applications - while displacing the use and cost of contractors. 
2. IT departments are already using IDEs and reducing their use of specialist tooling.  Platforms like Microsoft .NET and Ruby-on-Rails have done much to consolidate tooling for developers.  What Codeless platforms do is FURTHER consolidate tooling.
3. IT leaders are already finding ways to embed IT development skills ever closer to the domain expert communities that use applications.  What codeless software does is take development out of the back-room into the front-office workshop so that developments produce faster, better results that the users and stakeholders WANT to use because they were instrumental in the design of applications they use.
4. IT leaders are already rationalizing the numbers of technologies they support in the enterprise technology stack.  What codeless software does is further reduce the footprint of technologies employed across the enterprise to install more commonality in development methods and tools.
5. Arguably the biggest change factor then with codeless workshop development lies in the impact of an IT department being able to keep-up with the ideas and process changes of the enterprise.  This has surprisingly big organizational design implications.  For decades people in the business have been able to BLAME IT for failing to support their domain needs.  Consider what happens when suddenly it does:  No longer is IT the scape-goat.  Also consider the impact of embedding software development into improvement teams as some innovative companies like AUDI GROUP already do.  The analysts that scope and build the applications are PART of the improvement team, incentivized by the impact of the process changes they manifest rather than being paid for ‘writing code’. Departmental and operational managers will be inevitably more responsible and accountable for IT as part of their function.  They have no choice but to consider the performance of the IT they use as being part of their portfolio of responsibility.  The role of the CIO becomes one of guardianship of IT resources rather than the deliverer of every single IT initiative.

  Keywords:  Waterfall, Agile, Codeless Software Development, CAAD, Encanvas, Software Development Methods, ALM, Application Life-cycle Management

Secrets of Going Codeless

How to Build Enterprise Apps without Coding

Building applications without code still requires methods and skills. In this article I describe the methods that Encanvas has used for well over a decade to successfully deliver situational applications developments.

The first application created on Encanvas in a codeless form was a silly form for appraisals.  That was in 2002 I seem to recall.  Since then the size and scale of applications authored 'codeless' have included several enterprise-wide systems, a few pan-regional systems and even industry-wide systems.

In the early years of Encanvas, most of the applications were created by Andrew Lawrie (the creator of Encanvas) himself.  It was impressive enough that Andy could take an enterprise requirement and just 'make it happen' all by himself, but since then the community of Encanvas authors and architects has grown exponentially.

The types of backgrounds of authors too is wide and varied.  I've produced a few systems and my background is generally sales and marketing.  A couple of the guys are 'proper developers' that have sold their soul to the codeless god, while many are domain expert consultants, entrepreneurs, project managers and analysts.  What do they have in common?  They can all use a mouse I guess!!

As the scale of applications gets bigger, and the variety of author skills broadens, it becomes more important to adopt methods to optimize the authoring process. No question, it is possible without method to be able to produce results with codeless software, but then that places a 100% reliance on the talents of the practitioner.

CAAD came out of a series of 'interventions' to streamline the codeless authoring process that all hang together pretty well and perform certain key tasks well (like requirements gathering).

Hopefully you find the linked document interesting and easy enough to absorb.  If you have any questions, I'd be happy to field them.

Ian.

Keywords:

Enterprise Applications, Codeless Software Development, CAAD, Encanvas, Situational Applications, IT Strategy, Application Life-Cycle Management

CAAD Codeless Situational Applications Development Methods for Analysts

CAAD Methods

I produced this article to describe how analysts can build enterprise-scale situational applications time and again without coding based on the methods adopted by the team at Encanvas over the past decade.  The methodology hasn't changed too much over the past few years but we continue to learn from customers and practitioners.

It's only in the last three or four years that we've honed the use of Outcome Driven Innovation (ODI) and Blue Ocean methods as part of this approach.

One of the factors that we've found critical to success is to specify the requirements, outcomes, user group structures, reporting needs (RPRS Analysis) etc. BEFORE jumping into a meeting room and workshopping solutions.

It's great to work in a collaborative way but having the structures and outcomes buttonned down means that analysts can get to work on the boring 'basic modelling' of a solution and have something to walk around before users and stakeholders start iterating their design.

I hope you find it helpful!

I.

Keywords:
Codeless software development, CAAD, Software Development Methods, Agile Software Development Methods, Encanvas, Enterprise Situational Applications Development Methods

Mining Sub-Surface Spend Economies - The role of Federated Spend Analytics Platforms

Sub Surface Spend

Slideshare presentation on sub-surface spend

In this presentation I argue the case for federated spend management software tools as a vehicle to affordably source sub-surface spend economies in organizations where procuement teams have harvested the low hanging fruit and now need to consider digging deeper into spend behaviors to achieve the next stage of procurement economies.

Key words:
Sub-Surface Spend, federated spend analysis platforms, encanvas, situational applications, data mashups, actionable insights, procurement strategies

What is a Customer Scientist?

I'm not sure it's classed as a profession yet, but I expect the discipline of Customer Science will one day have professional associations, events and college courses attached to it.

The marketing industry has come a long way from the Micheal Porter days where companies focused on their chosen market and worked out how to compete with incumbent competitors.  Markets are much more fluid than that nowadays, so are customers, so too are competitors.  It would be so nice if customers, competitors and markets would stand still for a while so we market analysts and marketers could study them and draw our conclusions on the unmet needs that could make wonderful new products and services, the communications messages and stories that would ring true with customers to make cutting-edge campaigns, to draw down on the things that customers really care about to create world-class customer satisfaction and fabulous retention figures.  But alas, in the digital era and the land of global competition it's not to be. Marketers must think, plan, test, prove and act while their customer landscape shifts like sand under their feet.

To succeed in this event-driven, always-on world, organizations require a new breed of customer-focused experts that combine the skills of the analyst with the creative bent of a marketer.  Enter the Customer Scientist.

A customer scientist is someone appointed with the responsibility of managing the process of accelerated business innovation; creating a pipeline of better ways of working that lead to better products, more successful promotional campaigns and happier, more loyal customers.  The literal definition of a customer scientist is someone engaged in... 'the intellectual and practical activity encompassing the systematic study of the structure and behavior of customers through observation and experiment.'

There is no single tool-kit or method available to discharge this role; although the methods and tools are gradually combining to form a coherent approach based on proven best practice.  In this world where customers are analyzed in near real-time for the decisions they make online and in retail outlets, no longer is the role of the marketer to sit behind a desk and plan a series of campaigns for the next six-months: everything is much more fine-grained, event-driven, targeted and tested.  Instead of running one or two campaigns that reach the top 10 or 20% of customers, customer scientists seek to serve the unmet needs and buying aspirations of every customer.  This is achieved by a constant cycle of coming up with a hypothesis, designing, testing, analyzing, re-iterating, testing again, tuning and acting on what matters.

Thankfully, while the environment within which marketers operate has changed, so too have the tools at their disposal.  Cloud CRM data-marts are the latest generation of tooling for marketers - and those very expert customer insights analysts - the customer scientists.  These platforms assimilate customer-related data from wherever its held and bring it together in a customer database that's in a near constant state of flux - a data-mart.  This data environment is a transitory snap-shot of the data and events happening within a business (and its supply-chain) relating to customer purchasing behaviors and interactions.

In addition to its role in assimilating and presenting a single version of customer data, Cloud CRM data-marts offer customer scientists an environment within which to experiment, develop hypothesis and prove or disprove them.  Tools are provided to segment and cluster customers to give a clearer picture of consistencies in structures and behaivors.  Yet more tools are provided to analysts for visualizing, sorting, filtering and sharing their analysis, and STILL MORE tools are provided to enable analysts to create situational applications to act on their learning lessons and acellerate innovation in the enterprise by tweaking processes, systems and working practices.

In this presentation I introduce the topic of Customer Science.  I hope you find it interesting.  Do let me have your feedback!

Ian.

Customer Science in Motor Retail: Keynote 2. How to Harvest Actionable Insights from Dealer Management Systems

Most motor retailers employ a Dealer Management System (DMS) to manage and administer all facets of their business operations.  The typical components of a Dealer Management System include Financials, Showroom and Customer Relationship Management (CRM), Parts, Service, Workshop Management, Point-of-Sale, Franchise Data, Vehicle Stock, Purchasing and Pricing Administration.Actionable Insights are those views of data that cause managers to ask new questions about how processes work and take action.  They differ from key performance measures and daily operating control (DOC) reports that focus on delivering a picture of progress against a strategic objective, operating budget or forecast.  What actionable insights can dealers expect to source from their Dealer Management Systems – and how do they source them?

...

Many of the Dealer Management Systems that exist in the motor retail sector are proprietary systems and some of the technology employed dates back to the pre-noughties when the world had never heard of cloud computing and mobile phones were the size of a small brick.

The reason why the industry has been so slow to adopt change in IT has a lot to do with the complexity of the dealer information environment and the close synergies of the many operational areas that exist in a dealership.  As part of a tightly knit supply chain, dealers also need their systems to talk to the systems operated by the motor manufacturers.  These integrations are also complex, hard to create and adapt. Dealers ideally want to have a single information environment so to meet their need. This means software vendors have to invest hugely in attaining the ‘critical mass’ of capabilities the industry demands before they can hope to compete with incumbents.  This creates a natural barrier to new software vendor entrants hoping to enter the market.

The consequence of this market stagnation in viable competitive DMS options for major dealer groups means many are required to operate computer systems that are poor at sharing their data and exposing the actionable insights that dealer execs need.

In a market where competition is growing daily, dealers know they need to be much better at understanding the relationship with their customers, better at producing personalized offers that are event-driven, fine-grained and relevant, and better at finding more reasons to speak to customers that are ever more distant and only happy to engage with suppliers when it suits them.

Data Discovery from Dealer Management Systems

The quality and completeness of operational analytical tools found in Dealer Management Systems varies hugely.  Many of the younger software products include highly impressive reporting tools that are ideal for understanding the performance of pipelines and processes.  Where most dealer systems tend to fall down is their ability to source ‘holistic’ views of customer profiling, interaction and life-time value.

The essence of customer science is to gain a single page view of the customer that uncovers all aspects of the relationship including:

• Profile: Affluence, location, family status, employment, habits etc.
• Persona: What makes them ‘individual’
• Communication: Communications history, activities and preferences
• Driver behavior: How the customer drives their vehicle and the impact on vehicle depreciation and maintenance schedules
• Buying behavior: What do they buy, how do they buy, likes and dislikes; what sort of relationship do they want to have with their suppliers?
• Products: What vehicles do they have, like, have had previously?  What other products and services have they used, would they like?
• Life-time value: What is the potential value of business derived from this individual

Uncovering these insights is valuable – useful - but it’s only when these many attributes can be connected that the eureka moments occur and marketers and their customer service colleagues uncover the gold dust that makes their campaigns more effective and raises the bar on their customer experience.

As a general rule, Dealer Management Systems are designed to support the many key processes that exist within a motor dealership.  By analyzing generic capabilities of motor dealerships we now know there are approximately 136 major processes that occur within a motor dealership.  These are supplemented by close to 200 common reports and approximately 150 primary referencing tables.  You get the picture – it’s complicated.

When software applications are built for a purpose the data models that underpin them take on that form.  In the case of Dealer Management Systems that means that most data models are poorly designed and include the same tables time and again in different parts of the data structure for different reasons.  For example, the field ‘Country’ will appear in customer and supplier addresses.  It will also appear in delivery locations and other records.  But will there be a single table for ‘Countries’?  Probably not.

So what?

The implication of this confused data structure is that execs that want to see customer views by activity, service used, spend (etc.) won’t be able to easily source these views because the data will be held in many separate silos and in the wrong structures too.  It also means that dealers, when they look at the data, they won’t necessarily know if they’re examining a list of vehicles from the CRM system, or a similar list of vehicles from their Vehicle Stock Book.

Advances in ‘Actionable Insights’ Data Harvesting

The good news is that cloud computing has introduced smarter ways of harvesting and creating new views of data that remove the hit and miss complexity of constantly trying to mash views on a spreadsheet.  How it basically works is this:

1. Flat files reports (the type you produce on a spreadsheet with no pivot tables) are produced for each master process area.  These are designed to contain the magic fields that identify customers, vehicles, sales people, dealerships and other key identifiers.
2. These flat files are uploaded to a ‘sorting’ engine that normalizes the data, uncovers errors and brings the various flat files together in the form of a relational database ‘in the cloud’.  This new data structure is sometimes called a ‘data mart’ – not to be confused with a ‘data warehouse’ which is a much more rigid, pre-planned data architecture for preprocessing views of data. 
3. Once the data is onboarded, data connections are automatically generated between the variousmagic fields (i.e. primary key identifiers of the subject records) and it’s now possible to start performing queries on the data to create the actionable insights execs want to see.

The above summary ignores the fact that there’s a lot of data NOT on Dealer Management Systems that can enrich customer profiling systems and add fresh perspectives to analysis such as customer experience data, website clicks and interactions, finance company data, service plan data, SMS messaging inbound/outbound activity, mapping resources and consumer profiling data resources from third party vendors.

Federated operational insights systems like the one I describe are best placed to harvest data in this way and remove the complexity and cost of sourcing data from Dealer Management Systems.  A few words of warning however:

• This type of system does not displace the need for effective operational reporting within the process-centric tools that run the business, though these are increasingly well catered for by vendors
• Operational analytical systems are rarely able to feed data back into the Dealer Management System, although why you’d need to do this I struggle to understand.  I suspect most managers want a ‘single version of the truth’ for the data they look at but few people care if it resides on one system nowadays.

Key Technology Components

The good news is that cloud computing has introduced smarter ways of harvesting and creating new views of data that don’t require people to invest time manually authoring reports or playing with data in spreadsheets – and thank goodness because anyone that’s had to do it knows how boring and tiresome to task of manually aggregating and normalizing data can be! 

There are a few crucial technology innovations that have made this possible:

Web Server Apps

Popular platforms like the Microsoft Web Platform (comprising of modules including Microsoft SQL Server, Microsoft ASP.NET and Microsoft IIS) are able to publish applications in a format that means they can be viewed on a browser without requiring any downloads and plug-ins to the computer device.  That means you can view applications using mobile phones, tablets, PCs, laptops and smart digital televisions provided you have suitable User Permissions to login. Users are assigned to User Groups and their access and usability permissions are governed by the permissions assigned to the group, or groups, they belong to.  In a 24/7 world, users can experience the Martini moment of – anytime, anyplace and anywhere computing without having to fuss with onboard apps.

Private-Cloud Infrastructure

Deploying hosting web server apps on a private-cloud infrastructure means that buyers don’t need to fuss about installing web servers and managing them.  It removes the burden of installing hardware and software applications; and managing them.  Private-clouds can be configured for each account (so that each account has their own dedicated database and resources), or alternatively clouds can be multi-tenant environments where customers share the computing resources of the host web server.

Data Flow Augmentation and Data Extract, Transform and Load (ETL)

Something has to engineer the automated uploading, normalization, transformation and workflow of data to the cloud environment.  The tooling to do this now is relatively common-place.  What this automated routine needs to do is to watch a folder and wait for reports to turn up, or automatically kick into life at a scheduled time, to then audit the content and coax the data into the technology equivalent of a food blender that will spit out ‘good data that fulfils the upload criteria’. 

One of the clever features of this ‘blob’ of technology is that it needs to be capable of dealing with iterative changes to successive uploads of the same reports.  Let me have a go at explaining this:

When you upload several reports to the data crunching engine in the cloud for the first time, all is good with the world because there’s nothing else up there.  Do it again and the data crunching engine needs to sift through the fresh upload of data and work out: (1) What data exists and hasn’t changed? (2) What data exists but has been updated? and (3) What new records have been added that need to be included?

This is a non-trivial challenge and is an important feature of the data crunching engine.  Otherwise, the next time data is uploaded it will result in duplicates and lots of confusion (not good!).

Operational Analytics Software

There are many different sorts of operational analytics software (see my summary article on http://www.business2community.com/business-intelligence/operational-analytics-best-software-for-sourcing-actionable-insights-0560328 for more details) but some kind of data visualization and reporting tool-kit is needed to make the most of the harvested data.

Combine these four attributes – web apps, private-cloud deployment, data flow augmentation/ETL software and operational analytics software – and you have the essential ingredients of an Actionable Insights system that can harvest data from your DMS without have to work hard to do it!

Purchasing Options

There is a wide spectrum of technology vendors able to provide either components or complete platforms for authoring operational insights systems capable of extracting data painlessly from a DMS. There are also companies out there that provide marketing, data cleansing and customer science services targeted towards serving the specific needs of the dealer market. 

When it comes to the computing platform itself, as I summarize above, any solution needs to manage data crunching (to get the data in the right shape and form) together with data visualization and reporting tooling to make sense of it.  These ingredients with then need to reside on a server somewhere; more commonly now on a private-cloud to prevent the need to manually install hardware and software.

Buyers have a range of sourcing options to consider and these include:

1. Build your own system by using a highly dexterous and enterprise scalable cloud platform

There are too many of these to cover all of them but examples include the obvious BIG names likeMicrosoft Azure, cloud.google.com/appengine, Force.com (from Salesforce.com), WebSphereClouds orAmazon Cloud.  These big cloud tool-kits make development much easier than it used to be and all of the major vendors are on-point when it comes to scalability, security and resilience.  Don’t expect to have a quick solution though because it takes time and effort to get solutions ‘tuned’ to suit your needs.  Nevertheless, once you’re done you should have a first-class system.

In the last few years we’ve also seen a proliferation of smaller, expert cloud app platform vendors likeCloudFoundry, CloudAppStudio and ActiveState that make it easier to design and publish custom applications to secure cloud environments, often demanding lower skills overheads.  The challenge facing these vendors is to ensure they can offer sufficiently complete platform capabilities.  They need to satisfy customers that they’re able to go the last mile on developments: The last thing customers want is to find, after months of a development, that some capabilities are missing and they have to start over on a bigger platform like IBM WebSphere, Amazon Cloud Web Services or Microsoft Azure that profit from the many millions of R&D dollars that bigger vendors have at their disposal.

2. Buy breed tool-kits

Getting a solution up-and-running faster can be achieved by selecting best-of-breed components. Two will be necessary to ‘fast-track your development;

1. A platform for designing the apps you need and managing the cloud data crunching using platforms like Interneer, Encanvas and OutSystems that offer out-of-the-box tooling to meet the majority of needs.  All of these providers provide ‘low or no’ coding overhead to the task of authoring applications.  They also possess rich data connectivity and workflow features.
2. An overlaid expert analytics tool-kit like Qlikview, Tibco Spotfire, Tableau, Yellowfin, Birst,iDashboards, Jedox and Jaspersoft.  These providers offer data visualization, dashboarding and reporting tools.

Of course, the nature of competition means that if you ask any of these vendors they will probably say they can offer a complete solution ;-)

3. Pay someone to build it for you

There are many companies out there like CSC, CACI, Canon, US Tech Solutions, Wipro, TCS, PA Consulting and countless smaller industry expert companies like DealerSolutions, Nybble and Ambridge Consulting that specialize in the data centric custom applications authoring arena.  These companies will provide fixed price projects to deliver solutions to buyer specifications.

4. Buy a ready-made service

I have no doubt that, like NDMC Consulting and introhive, there are other companies have entered the automotive market in the past few years thanks to the available of suitable tools to provide ready-made online services that deliver the outcomes needed without requiring dealers and motor manufacturers to build their own solutions.  The challenge facing providers is that each and every system will require heavy customization and tailoring.  Encanvas Remote[Spaces] is an example of a cloud architecture that enables every customer to enjoy their own individual and private cloud service.  Competing platforms like Interneer,Tibco Spotfire, Force.com and OutSystems (to mention a few) are no doubt able to offer a similar capability.

Summary

Cloud-based operational analytics solutions are growing in popularity as a way to extend the life of Dealer Management Systems investments.  They lever value from sleepy data held in administrative systems by finding smarter ways to turn data connections into new reasons to engage customers in dialogue.  These solutions do create a single version of the truth but in a different way; not by seeking to install another system, but instead by adding a value extracting capability in the cloud that gives dealer executives the actionable insights they need without the pain of yet another major IT project.

About This Series of KeyNote Articles on Customer Science in Motor Retail

How does Motor Retail benefit from applying operational analytical tools to source actionable insights?  In this series of keynote articles I explore how the coming together of mobile computing, innovations in customer science and advances in big data analysis are creating the perfect storm for marketers in motor retail.

Customer Science in Motor Retail: 5 Actionable Insights That Every Dealer Should Have

How does Motor Retail benefit from applying operational analytical tools to source actionable insights? In this series of keynote articles I explore how the coming together of mobile computing, innovations in customer science and advances in big data analysis are creating the perfect storm for marketers in motor retail.

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Keynote 3.

5 Actionable Insights That Every Dealer Should Have

Actionable Insights are those views of data that cause managers to ask new questions about how processes work and take action.  They differ from key performance measures and daily operating control (DOC) reports that focus on delivering a picture of progress against a strategic objective, operating budget or forecast.  What are the top 5 actionable insights that every motor dealer should have?

…

Motor Dealerships are a complex multi-disciplinary business operation.  The aspects of activity are so many and varied that managing performance and growth requires a clear head and clarity over the objectives that really matter.  Every dealership has a different combination of capabilities and franchises that add an extra layer of ‘uniqueness’ to the reporting systems required.  So is it possible to pinpoint just five actionable insights that all dealership exec teams should have access to?  I believe it is.  In this article I suggest my top 5 list of actionable insights together with an explanation of why they’re important.

1. Consolidated Performance Scorecard

A scorecard is a dashboard that incorporates a series of objectives that an organization is striving to achieve.  In most organizations the number of objectives defined in a scorecard is in the order of 12-16 as any more becomes unmanageable.  A consolidated performance scorecard provides a topline view of progress towards business outcomes.  It’s essential for any organization to know how it defines success and seek to achieve it.  Scorecards are therefore valuable and useful management tool to articulate and communicate progress towards strategic outcomes – BUT… the point about objectives, and the key performance indicators used to measure progress towards them, is they should be ‘stuff’ that managers already know to be important.  This type of reporting is therefore ‘handy’ but it would be difficult to argue KPIs are ‘actionable insights’ as they rarely encourage managers to take action in themselves.  What starts the journey of objectives and KPIs becoming ‘actionable’ insights occurs when managers can drill-down into the data that is the source of the KPI.  When managers drill into data and ‘become curious’ the magical and mysterious kapow! explosion happens that leads to processes being improved, new initiatives tabled and the like.   A Consolidated Performance Scorecard gives the entire management team a snapshot of how THEY are performing to meet strategic outcomes.  It’s my FIRST actionable insight because it STEERS the ship.

2. Consolidated DOC Side-by-Side Dealership Analysis

The Daily Operating Control is a report that summarizes how each revenue generator and discipline of a motor dealership is performing.  What normally gets covered are the revenue generating pipelines of the dealership including MOTs, parts sales, red and amber Vehicle Health Check (VHC) / deferred works, service hours, warranty repairs, oil, tyres, bodyshop repairs and more.  Most dealers I know favor a side-by-side analysis of branch performance because it encourages competition – and the comparison between franchises and dealerships makes for interesting reading (to understand which business units are performing well, which are performing poorly, all judged on the same scale given that organizations commonly struggle to compare performance between business units when managers seek to qualify ‘what good looks like’ using their own scale.  The Consolidated DOC gives managers across the business a daily progress report and a month-to-date summary that provides opportunity to correct sub-optimal performance before it’s too late.  It is the epitome of ‘actionable insights’ in the operational reporting world and becomes my SECOND ‘must-have’ actionable insight for dealerships.

3. Customer Persona Profiling Summary

It’s always been important to manage customer relationships one customer-at-a-time as Don Peppers andMartha Rodgers put it but as customers become ever more distant and empowered, the need to build digital personas has moved on to a new level of priority.  I’ve struggled to understand why the Motor Retail industry has been so slow to adopt customer profiling and persona building when retailers such as Tesco with their Clubcard system and online companies like Amazon have so clearly demonstrated the business rewards, but I do see this initial reticence is changing as more dealers become ever more aware of the potential to boost growth by simply harvesting their most important asset – their customer relationships. There are many sides of a customer relationship – communications preferences, activity history, contact points, location, buyer behavior, life-time value, future life-time value, affluence… - and it’s when you combine these views that exciting things happen.  There are many ways to build personas and through them, marketers and sales leaders see patterns of likes, spend habits and relationship characteristics that lead to new campaigns and better ways of sourcing customer value.  A customer profiling persona summary presents a one-page view of the many different facets of your customer community.  It covers not just ONE customers’ characteristics but overlays ALL of your customers in one ‘map’ of your customer universe.  It makes for very interesting reading and becomes my THIRD actionable insight.

4. Customer Happiness Index

Customer happiness matters because it’s the lion in the cave that comes out to bite your business unless you keep an eye on it.  There are many ways organizations can measure and monitor customer happiness but dealers can still struggle to place a ‘happiness index’ on their business that produces a snapshot of happiness and trending.  In addition to manufacturer sponsored analysis, many dealers are creatively sourcing happiness data by using simple tools on their websites and paying third party.   I’m also a big fan of soft-measures that systems like www.tag-check.com are excellent at sourcing.  You can’t hope to buildLoyalty Beyond Reason when your customers are unhappy; it’s even a big call to ask for respect(sometimes described as love in plain clothes!).  Installing a customer happiness index is my fourth actionable insight.

5. Mapping Customers and Qualifying Addressable (Local) Market

Understanding the opportunity of a dealer within their locality helps to qualify how well a dealer is doing compared to how well IT COULD be.  It’s not difficult to plot customers on a map.  In countries like the UK and the Netherlands mapping customers is made easier by the quality of the postcode system.  In other countries like the United States and South Africa, postal codes are not very accurate and it takes street address details to accurately plot customers (far less convenient!).  Once customers are plotted it’s relatively easy to assign customer attributes to records so map views can be filtered by dealership, sales team, sales person, affluence, persona, outstanding MOT reminders – whatever.  If your customer records include a CAMEO (‘affluence’) profile, companies like www.improvemydata.com  make it pretty easy to obtain a listing of similar customers in your region.  This makes it possible to understand your MARKET SHARE! How cool is that?  Maps are great!! I’ve never produced a mapping application that hasn’t shownsomething interesting for dealers to see.  That’s why creating a map view of the customer landscape and qualifying the addressable local market is my fifth actionable insight that every dealer should have access to.

The list above is MY top five.  If you have other opinions or examples I’d be very interested to hear about them!

Real-Time Data Diagnostics - How Encanvas Takes Agile To A New Level

Have you ever created an application only to find it doesn’t work?  

Sounds crass doesn’t it.  The fact is that most manually programmed applications have errors in them.  But modern codeless applications design environments like Encanvas overcome the majority of programming ‘hick-ups’ by auto-generating the code that underpins authored applications through the use of drag-and-drop, point-and-click and wizard based interfaces.   That means almost no testing, tuning or re-working costs for business IT projects.

This is important to the software and data integration industry that has a pretty sour track-record of project delivery.  The article ‘Why Your IT Project May Be Riskier Than You Think’ by HBR (November 2011), that followed a survey of 1,471 IT projects with an average spend of $167m, found that:

• The average overrun was 27%
• One in six of the projects studied was a black swan, with a cost overrun of 200%.
• Almost 70% of black swan projects also overrun their schedules.

One area that has remained to challenge the software development industry has been how to overcome data integration errors on deployed applications.  No matter HOW a design environment links to data sources, such is the complexity of the task that it becomes almost inevitable that some data integration errors will creep in when developing a complex database-centric application.  For example, a field in a table could be re-named or deleted in the source database resulting in no data appearing on the published site page because the database is unable to find the requested data.  Sometimes it’s quite easy to track down these issues; other times – with complex applications – it takes a pretty neat understanding of the application and how the data hangs together in order to overcome the issue.  When applications designers have spent hours working on a great application, the very last thing they need is to fall at the last hurdle and find ‘the app doesn’t work’.

Thankfully, Encanvas now overcomes this problem area too with its new real-time data diagnostics technology.

Those familiar with Encanvas will know that the design of applications all takes place in ENCANVAS CREATE DESIGN STUDIO in a codeless environment.  It offers design elements, data linking and action linking features to build content and logic into applications using an easy to learn design interface that has the look and feel of a desktop application. What it actually produces are cloud deployed ASP.NET portals that present data in HTML so they can be used on desktop, tablet. Mobile and digital TV platforms.  While it adheres to Microsoft Enterprise Platform standards of security, performance tuning and scalability, it removes the use of programming and means application can be authored in workshops with users and stakeholders.

When applications are published to the cloud, Encanvas’s new visual diagnostic mode allows designers to review the data connections employed in applications using an innovative visual referencing system.

Each field is audited for its data connections and the results are presented on the screen.

Full details of connections are presented when clicking on the associated icon. Using the live data diagnostic features of Encanvas removes the last major hurdle in applications engineering for authors of business applications.

The roll call of applications authoring productivity features in Encanvas now includes:

• Codeless platform and server installation
• Codeless private cloud creation (and replication)
• Codeless data connectors
• Codeless information flow automation
• Codeless data mashup and automated data structure creation
• Codeless design elements
• Codeless logic link creation
• Codeless action layer automation
• Codeless KPI creation
• Codeless map creation
• Codeless upload/ETL automation
• Codeless download (export) with formatted reporting/printing
• Codeless social network creation
• Codeless graphing (and graphing logic) creation
• Codeless email workflow creation
• Codeless publishing
• Codeless UI design 
• Codeless federated user identity management augmentation
• Codeless site structure design
• Codeless and real-time data diagnostics
• Automated design element creation from data structures
• Automated database creation from designed data structures
• Automated lookup table generation
• Automated form creation

If you’ve yet to try Encanvas, it’s easy to arrange. Simply email sayhello@encanvas.com and request a secure private-cloud Remote[Space] to have a go at codeless situational applications authoring for yourself.

Big Data Training Workshop 1: Introducing Situational Applications

BIG DATA TRAINING WORKSHOP 1: INTRODUCING SITUATIONAL APPLICATIONS

Situational Applications are any software applications designed to serve the long-tail of demand from individual information workers (or small communities of workers) for ‘better ways to analyze data, capture it and act on it’.  They may be created for a moment and then discarded, or later adopted and absorbed into enterprise systems.  Sometimes they are unkindly described as throw-away or experimental applications but I prefer to think of them as the first step on the accelerated business innovation journey.  They are key to the world of BIG DATA because they are the tool-kit to apply learning lessons from data and turn it into better ways of working.

I’ve had such overwhelming feedback and interest on the Big Data Situational Applications articles I’ve penned that it’s encouraged me to write a few more that walk through how we use Encanvas to assimilate data from various sources, analyze it and act on what matters.  Of course there are a wide variety of Encanvas partners and solutions providers that offer ‘off-the-shelf’ industry solutions so in this series of articles I’m assuming that the application is something ‘custom’ or new that hasn’t been addressed before.

In this series of articles I plan to include the following topics:

1. Introduction to situational applications 
2. Data assimilation: what it is and how to do it
3. Operational Analytics: making sense of data with useful tools
4. ACT: designing apps to iterate processes one canvas at a time

The obvious article that’s missing is a training course on HOARDING technologies like Hadoop, Microsoft Business Intelligence, SAP hana etc.  The reason I’ve missed this step is because (a) I don’t know anything about it and, (b) In many cases you don’t need to hoard data and organize it before creating situational applications.

Just a few brief introductions before we start…
If you’ve not encountered Encanvas software before, it’s basically a business computing platform designed to exploit data held in different places and different formats by designing situational applications to analyze data and apply learning lessons. Unlike business intelligence systems and other platforms that HOARD data and create a data warehouse before you can do anything, Encanvas connects to, and organizes data into new structures by exposing data to designers in a consistent format to enable ‘drag and drop’ selection and formation of new data structures.  It’s really easy to use for business analysts, and it produces results quick, so it’s realistic to develop situational applications in workshops with stakeholders, users and sponsors present (yes really!).

Companies like USTech Solutions, NDMC Consulting, Ambridge Consulting, Nybble, SovereignTech and others use Encanvas as a tool-kit to author situational applications as a service.


Everything you wanted to know about situational applications but were afraid to ask
The first ever situational application platform was the spreadsheet.  Times are changing.  Information workers need to deal with much bigger data sets and IT leaders must ensure that data is organized, always-secure and any software application must adhere to necessary standards of governance and compliance.  The new generation of situational applications platforms like Encanvas are engineered to meet ‘IT hygiene standards’ of security, performance and scalability, yet they provide unrivalled self-service tooling to enable web workers to fully exploit accessible data resources.

History
Encanvas was conceived in 2002 by the founders of NDMC Consulting.  It was initially an idea (perhaps a belief) that in future, business users would seek to work in social groups that would span across and beyond enterprise boundaries and, having experienced this new form of business workplace, key knowledge workers and creative contributors to business processes would seek to be able to author applications for their communities in a form purposely sculptured to the needs of the community of use.

This idea of socially-centric, built-for-purpose and potentially thrown-away software was unknowingly endorsed by technology thought-leader Clay Shirky in his essay ‘Situated Software’ published in March 2004 when he wrote, “Part of the future I believe I'm seeing is a change in the software ecosystem which, for the moment, I'm calling situated software. This is software designed in and for a particular social situation or context. This way of making software is in contrast with what I'll call the Web School (the paradigm I learned to program in), where scalability, generality, and completeness were the key virtues.”

In August 2007, Luba Cherbakov and a team from IBM wrote the first of two articles on what they described as ‘Situational Applications’.  In their paper titled ‘SOA meets situational applications, Part 1: Changing computing in the enterprise’, Cherbakov and her colleagues defined the attributes of Situational Applications, stating, “The loosely accepted term situational applications describe applications built to address a particular situation, problem, or challenge. The development life cycle of these types of applications is quite different from the traditional IT-developed, SOA-based solution. SAs are usually built by casual programmers using short, iterative development life cycles that often are measured in days or weeks, not months or years. As the requirements of a small team using the application change, the SA often continues to evolve to accommodate these changes. Significant changes in requirements may lead to an abandonment of the used application altogether; in some cases it's just easier to develop a new one than to update the one in use. The idea of end-user computing in the enterprise is not new. Development of applications by amateur programmers using IBM Lotus® Notes®, Microsoft® Excel spreadsheets in conjunction with Microsoft Access, or other tools is widespread. What's new in this mix is the impressive growth of community-based computing coupled with an overall increase in computer skills, the introduction of new technologies, and an increased need for business agility. The emergence of Asynchronous JavaScript + XML (Ajax)—which leverages easy access to Web-based data and rich user interface (UI) controls—combined with the Representational State Transfer (REST) architectural style of Web services offers an accessible palette for the assembly of highly interactive browser-based applications.”

Today
Situational applications remain a largely misunderstood concept in enterprise computing due to pre-conceived notions about how IT works that are now proven to be fatally flawed.  These include:

• Applications that can be designed cheaply enough to ‘throw-away’ can’t possibly be expected to meet enterprise data integration, security, performance and tuning expectations.
• It’s not possible to create the critical-mass of building blocks and tooling required to remove the majority of programming overheads.
• Organizations are better off buying best of breed solutions, to then mackle them together.
• There is no competitive advantage to be gained from IT as companies now use the same platforms. 
• If players like Microsoft, Google, Oracle and IBM haven’t made it work then it isn’t possible!

In many ways the demand for situational applications has never gone away but, until the emergence of the BIG DATA story, the message of Situational Applications has struggled to find a voice within organizations as it is a genre of technology designed to meet the many and varied ‘small’ needs of a variety of communities. It has lacked any understandable clarity of purpose in IT architectures.  Instead of sourcing expert tools, CIOs with other things to think about have instead done their best with 'approved enterprise tools' – Content Management applications like Microsoft SharePoint and Enterprise Applications Platforms like IBM Websphere and Oracle Weblogic.

BIG DATA has led to the re-discovery of the important role of expert situational applications tooling as organizations acknowledge the need to equip analysts and middle-managers with self-service, community-centric applications to assimilate, analyze and act on the actionable insights they’re surfacing.  Technologies designed support the creation of Situational Applications are by necessity ‘codeless’ which means the authoring of applications is done using drag and drop, point and click or wizard based interfaces that automatically generate the programming code rather than it being authored ‘manually’.  This reduces errors in code.  It also means that applications can be authored in near real-time; very often within a workshop environment.  Having stakeholders directly engage in the authoring process creates better applications in a shorter time.

In the next article in this series, read about the first stage in Accelerated Business Innovation: Data assimilation: what it is and how to do it.

BIG DATA with Encanvas (infographic)

In this infographic I explain how Encanvas employs big data as its raw material to accelerate the pace of business innovation.  Of course, data doesn't have to be BIG to be useful.  Over the last 10-years I've seen many good ways of extracting value from data by simply having more data of different types accessible to business analysts. For example, one professional services company was able to improve their presentation of credentials by exploiting data from a third party database to enrich their customer records and map their customer records onto their project data to build a richer understanding of what they'd delivered (how and to whom) to improve the quality of their case stories.

Accelerating the pace of business innovation requires not just the means to harvest data and analyze it - businesses ALSO need the tools to improve their processes and embed learning lessons into their day-to-day activities.  This is by far no mean feat!

Encanvas overcomes the natural reticence of organizations to change, break age old 'norms of behavior' and embrace better ways of working by creating applications without asking organizations to invest in IT infrastructure or spend days, weeks or months on IT projects with no clear ROI.  Building a situational application in a day or half-day workshops session means that businesses can understand the value of data - and apply it - removing the risk and cost of process change.

Speaking to users of Encanvas, they will very often rebuild apps many times as they learn from their data and re-apply learning lessons to improve processes.  This would not be practical or affordable if a traditional software development or enterprise platform like Microsoft SharePoint was used to fashion applications.

The original infographic can be viewed on slideshare here.

BIG DATA - The Killer-App for Motor Dealerships

BIG DATA is a big topic at the moment in IT... and that interest is gradually working its way down the corridor to the marketing office and the boardroom.

In the Motor Retail industry new IT innovations emerge somewhat slower than other verticals because many dealers rely on partner suppliers to manage their IT.  This creates a bit of a lag in adoption of new technologies.

I doubt it will be long however before the topic of BIG DATA filters into motor dealerships because the competitive advantage it brings is compelling.  Another reason why BIG DATA technologies are likely to take off so fast in this market is because there is pent up demand in dealerships to 'get moving' with innovation.  Many of the Dealer Management Systems employed today are WAY behind the curve on technology - some are not even based on web technologies.  The fragmented information environments that dealers have to cope with day to day are embarrassing to anyone in the computer industry (on behalf of all us nerds in the software in 'sorry guys').

At NDMC we're leading the charge to introduce BIG DATA solutions into the Motor Industry and the level of interest and take-up has been rather overwhelming.  In this presentation I've provided a short summary of the role and impact of BIG DATA for anyone who's yet to encounter the technology.

I hope you find it informative and a little fun! I.

BIG DATA - The Killer App for Motor Dealers from Ian Tomlin

Bloomberg interview with CEO of APT exposes BIG DATA value benefits

This is an interesting propaganda story by APT - interesting how US companies get so much air-play for things that UK companies like Dunn Humby have been doing for years ;-)

What's more interesting to me is that APT's perspective on what Big Data 'is' is so similar to NDMC's description of Data Investment Management with its end-to-end process life-cycle of discover data, interpret it and act on what matters.

I.

Benefits of Profiling Customers For Motor Dealers - And How To Do It

Customer Science - ...building deeper, richer, more personalized customer experiences by applying customer insights to better understand what customers care about, how they think and act, how they make buying decisions, how they want suppliers to communicate with them, how they want to be treated, what they expect from their suppliers... and more.  But does it work (is it working?) in  Motor Retail? Is it being used successfully to turn 'sleepy customer data' into a dynamo for new reasons to speak to customers, turning clicks and visits into cash? 

...

Since the 1980's I've watched with interest as the methods adopted by marketers to engage customers and prospects have adapted as new technologies and created approaches have come of age.  More than in any era before, the noughties has required marketers to take on a cacophony of new techniques to engage customers that want the perfect mix of a personalized customer service experience and relevant, timely offers while not wanting to be barraged by suppliers with direct marketing and unwanted contact.  The growing impact of the Internet, mobile communications, social networking and a more techno savvy customer communities is changing the rules for marketers on how to get the biggest impact from their marketing spend.

By now, the marketing industry has worked out that it's vital to gather information on customers 'as part of the day job' to build up a profile and persona on each and every customer so the customer enjoys a personalized 'shopping' experience.  Companies like Tesco and Amazon have shown the way in exploiting customer data to sensitively give shoppers what they want rather than forcing them with a strong hand down paths they don't necessarily want to follow.

The type of relationship customers want - location-centric, timely, fine-grained, event-driven offers personalized to their particular wants and preferences - can only be achieved in my opinion by taking every opportunity to learn about customers through each and every interaction.  Customers don't want to be contacted out of the blue, or left standing in service receptions JUST SO THAT DEALER CAN CAPTURE DATA TO SELL TO THEM.  For Motor Dealerships that means not only investing time into collecting data 'as part of the day job' but also investing in the PROCESS of 'digital persona-lization'.

In an odd way, customers generally WANT suppliers to build and know their digital persona because they want that type of personalized experience (with the suppliers they want to buy from).  What they definitely DON'T WANT however are relationships with suppliers that abuse their trust or over-bite on the level of relationship they want. 

Marketers in most industries profile their customers these days, but 'Customer Sciences' are only now appearing in the Motor Retail sector.  This is partly no doubt down to the fact that in certain countries and regions, dealerships with the right franchise have very little local competition for the brand they promote. While it's always silly to generalize, the feedback I get is that this is changing and levels of competition are growing in most geographies.

Understandably, dealer principles want to know:

• How does profiling help me to sell more?
• How do I build up profiles and personas from my data?
• How do I enrich my data when it's poor?
• How do I channel dialogue opportunities to encourage sales people to engage customers at the right time and for the right reasons?
• Where do I start?

So here I attempt to answer these questions:

How does profiling help me to sell more?
When dealers have a better idea of the sorts of customers they sell to they become more adept at appreciating the types of offers that work.  Knowing the affluence of customers on your database for example means that you can find out (from third party agencies like www.improvemydata.com) the addressable market in your locality by mapping these target customers against your existing database. Marketers and dealer execs can use the profiles they hold about their customers to understand how to maximize their revenue potential per customer - to then work out which customers are not achieving their anticipated life-time value. Sometimes enriching data can seem like a 'painting the Fourth Bridge'  experience where you need to start again every time you think you've finished.  Focusing on the most important customers first and devising marketing strategies to grow value in the customer segments that matter most allow marketers to drive optimal value from the smallest efforts.

How do I build up profiles and personas from my data?
There are many ways you can profile customers - the most obvious being:
(A) Life-time value - It's quite easy to derive a value for each customer based on assumptions of what they should spend over their lifetime.  Many motor manufacturers hold and share these insights for each model they sell.  Comparing your revenue per model against the forecasted return helps to qualify 'where things are going wrong or could be improved.  Seeing this data, dealerships can realize that some makes and models are more reliably generating the life-time revenues they should than others; perhaps because some makes and brands face more local aftersales competition than others.
(B Buyer behaviour - At NDMC we define the buyer behaviour of private vehicle buyers through fourn buying personas:
 

1.  Cherished Teddies > Loyal buyers that consistently reach within 20% of their future planned life-time value. Typically these customers will purchase service or loyalty plans (often with additional insurances or paint protection options) to make sure their dealership can look after their needs. For this group dealers should have a very complete picture of the customer profile.  Cherished Teddies need looking after because they are the group most likely to recommend others buy their vehicles from your dealership!
2. Loyal Dogs > Vehicle buyers that purchase after-sales products but don't achieve the future planned life-time value.  Understanding why this group doesn't achieve their future life-time spend is helpful because it can point to weaknesses in your offerings or aspects of local competition that you're not aware of.  At the same time, it pays to contrast buyer behaviour with affluence ratings given that it may well be that Loyal Dogs WANT to be Cherished Teddies - they just can't afford to be ;-)
3. Cats > Buyers that have purchased a vehicle from you but only come back for aftersales services when it suits them.  Just like cats you can't rely on their loyalty and you have to work harder to get their attention.  Cats are harder to love because they're not around as much. The obvious thought is 'What does it take to turn a Cat into a Loyal Dog?'
4. Neighbours' Cats > Buyers that haven't bought from your dealership but have bought services or parts. Perhaps these customers are 'sampling your dealership' to understand how well they get treated. If you pay them more attention, perhaps they will become your Cat in time.

(C) Affluence - How much money people earn is a good indication of how much disposable income they have to spend on a vehicle and indeed the sort of vehicle they might want to purchase.  Consumer data can tell you a great deal about who your current customers are and the target people in your area most likely to be willing and able to purchase a vehicle from you.
(D) Contact activity and preferences - It helps to understand these days 'HOW' customers want to communicate.  There is a significant shift to social media and mobile methods. Assumptions that 'mobile and Internet' tools are for the young are usually baseless. Many 'people that want to be young' are avid smartphone and Facebook users (My mum twitters all the time!).
(E) Location - Location awareness is a powerful market tool.  These days it's quite possible to focus events and campaigns to target specific geographies that have a proven bias towards your brand based on affluence or locality. Consumers can also be targeted 'on the hoof' if you have the means to engage them when mobile.
(F) Arbitrary Banding - Even when all of the above fail, marketers can start their entry into Customer Sciences by thinking about their own arbitrary bands based on a selection of customer metrics and see what falls out.  Dealer execs have a very good feel normally about their customers and what works and doesn't work.  Exploring these perceptions and seeing if the 'data' backs up the assumptions can be a good place to start for organizations that have never experimented with Customer Sciences before.

One other thought - unsurprisingly, when you compare these different aspects of profiling together it helps to build up a visual image of the 'persona' of the buyer and this can help to further develop the rapport with the customer through a 'deep support' understanding of their wants and needs.

Building up profiles is a question of understanding key data metrics and then validating where the data needs to be sourced from.  Sometimes data is already held in admin systems (like Dealer Management, Showroom or Service Management systems), while other times it will need to be captured by installing new systems or methods.  Not all methods require manual data entry.  These days, customers are often prepared to enter their own data provided there are rewards for doing so (such as gaining free access to an online portal that provides details on their vehicle valuation and the impact of their driving behaviour).

The life-cycle for Customer Science (i.e. creating and leveraging profiles) goes something like:
(1) Harvest - Gather and cleanse data from its various latent sources
(2) Make Connections - Build new connections between data items to produce new metrics
(3) Personalize - Apply the learning lessons to personalize the dialogue with customers and create new reasons to interaction with more relevant and timely offers
(4) Learn - Measure the effectiveness of personalized interactions and learn from them to source new ways of bringing value

Like most processes in business, the first job is to recognize that the process needs to exist and, having formalized it, it becomes something that can be measured and improved to become progressively more effective.

How do I enrich my data when it's poor?
In the motor dealership arena, the most frequent response I encounter is 'Sounds great but my customer data quality is so poor it wouldn't work for us."  Wrong, wrong, wrong.  It's not that difficult to improve the quality of data these days. The weapons marketers can use to enrich data include:
(1) Paying external agencies to manually enrich data (very expensive)
(2) Progressively improving the quality of data over time by becoming more robust in making sure members of staff complete records more consistently; some of which can be enforced with changes to software
(3) Exploiting 'big data' to cleanse your data by straining it through a source of 'good data' such as an industry database, consumer insights database or postcode database.

How do I channel dialogue opportunities to encourage sales people to engage customers at the right time and for the right reasons?
Systems like NDMC's LeadGenerator360 enable dealers to upload/mine their existing data from administrative systems and build up a pipeline of reasons to speak to customers when they want you to (I'm sure there are other systems that work in a similar way out there ;-).  Such systems generate a pipeline of reasons to engage customers 'one customer at a time' based on key events like vehicle birthdays, service plan expiries, warranty expiries, MOT reminders etc. that ensure every single worthwhile opportunity to engage customers is not overlooked.  At the same time, leads are allocated and load-balanced in a way that avoids sales people from becoming overburdened. Linking lead pipeline to customer profiling builds a virtuous circle of 'using insights to capture insights' that reduce the need for contact 'expressly to capture data we should already know about our customers' or spurious sales calls that customers hate because they feel they're being sold to.

Where do I start?

The best way to start the journey towards Customer Science based marketing methods like profile and persona building is to perform an audit of the 'net present state' of your customer insights and the extent to which your dealership is exploiting its dealer insights. 

NDMC Consulting offers a 'CRM Diagnostic' service and system.  This is a one-time reporting cycle where key data is extracted from existing DMS and administrative systems to a secure space, where it is cleansed, normalized, analyzed, and then - from the connections made in the Customer Science Engine - out pops a series of online (still secure) 'drill-downable' reports and views.  This type of diagnostic service will qualify how complete and consistent the customer data is, and the number of 'meaningful reasons to engage customers' against what the dealership should be capable of based on the size and characteristics of their customer database.

This article was originally published in DrivingSales.com. To read the original article click here.

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