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.

Profit from BIG DATA: Using actionable insights to accelerate business innovation

Actionable insights are those that make managers and workers 'curious' and cause them to act. The big data revolution is giving organizations many more reasons to exploit data so they can improve customer experience, personalize offers, improve sales engagement methods, optimize processes and surface BLUE OCEAN opportunities.  Even your existing data that's locked away in back-office systems can become extremely value when combined with other data to answer new questions. But what PROCESS do you need to harvest actionable insights and act on them to accelerate the pace of innovation in your business?

In this article I've summarized the obvious 5-steps to turning sleepy data into (first) actionable insights and (then ultimately) better processes using big data cloud computing and operational analytics technology.  Then I've explained the 2-step process (yep just two steps) that Encanvas makes possible. While the example below is based on the capabilities of the Encanvas platform because we're familiar with it at NDMC there are other technologies from Tibco, IBM and Oracle that you could also consider to achieve the same outcome (though this is probably an 'IT' project if you do).

The reason we developed and continue to use Encanvas at NDMC is because it produces the fastest time to value by reducing the number of steps in the Accelerated Business Innovation process - and it deskills the activity of process innovation so you don't need to be an IT expert or programmer to implement it.

A typical 5-step innovation life-cycle process using a mix of enabling tools is:

1. Capture Data -  2. Hoard Data - 3. Harvest/ETL - 4. Analyze -  5. Act

This can be a long-winded and technically challenging process: It requires a different bit of software for every stage, and this results in large IT teams, expert skills and slow time-to-value projects.

Why 'Acting' on actionable insights is like exploring for oil
The accelerated business innovation process is modeled around oil exploration where you first make sure you've hit oil before you start to build infrastructure.  This means that when actionable insights surface a better way of working, the solution is to create a situational application (at little or no cost) to validate the process change.  If the new process works better and provides incremental value then it is adopted, if not, it is discarded.  That takes a new approach to IT and new tools. The most challenging part of adopting an accelerated business innovation process using legacy enterprise software is therefore the 'ACT' bit.  Adapting legacy platforms like Oracle, SAP and Microsoft Dynamics is more akin to turning an oil tanker - really difficult and expensive:  It requires IT people with deep skills.  That's why we use Encanvas to serve the long-tail of situational applications that are used to 'explore' value and then adopt those applications that prove valuable as a layer that sits above the fragile 'heavy-lifting' enterprise software that most businesses operate today.

1. Assimilate data

Data assimilation describes the task of bringing together data to a level of consistency and completeness that makes it useful.  The term means more than simply harvesting data - because anyone who has tried to bring data together from different sources (even using a spreadsheet) knows how time consuming and painful it can be to extract, organize and structure data.  Fortunately, tooling is available nowadays to automate many of these very manual tasks.

Many people assumption - probably due to the norms of behavior encouraged by decades of data warehousing - that it's necessary to hoard data and organize it before you can start to lever insights from it.  Nope.  Today, the data harvesting, data source integration, data augmentatation and normalization tools are so powerful that most operational analytics systems can 'get at the data' wherever it is and exploit it without having to first HOARD the data in some great big massive repository that you have to pay for.   (That said, if you do want to hoard data, technologies like Hadoop have made the task of hoarding data of different forms immeasurably less expensive and more scalable.)

Encanvas, and technologies like it incorporate a full range of data connection and ETL tools that obviate the need to code or use lots of APIs and middle-ware tools in order to harvest data.  It supports data integration with CCTV, security databases, hardware, sensors and telematics.  Whats more, it enables situational applications designers to exploit structured data (like relational databases, web services, CSV files etc.) and unstructured data (such as PDF documents). Their Information Flow Designer application gives applications designers the means to formalize data gathering workflows from data sources, transform, cleanse and normalize data - and then expose it for operational analytics applications like Encanvas Create Design Studio to exploit.

One thing we realized at NDMC a decade ago is that authoring exploratory (situational) applications to source actionable insights requires designers to play-around with data, forms and logic tools - all the toys needed to create exploratory apps in the same integrated design environment. That means you need the data sources to play around with too.  Encanvas allows us to try things out, get things wrong and work with stakeholders until we get a result.  Assimilation of data is tricky, sometime painful and rarely straight-forward!

2. Create and Deploy Situational Applications

Sourcing actionable insights normally means connecting to many different data silos and exposing the data held within them.  The 'clever bit' that technology can help you with is to bring data together in new ways, to then formalize business processes to act on it.

This 'iteration' of the enterprise computing environment requires to perpetual creation of situational applications that serve individuals or small communities of users that traditional IT can't service because this long-tail of demand is simply too long to be addressed by technology platforms that need coding - and users have such a diversity of requirements for tooling.  While there is a natural instinct to employ existing tool-kits like IBM WebSphere, Microsoft SharePoint or applications platforms like Force.com, art NDMC we foudn this to be a costly approach because it requires large IT teams and the tooling is limited by the capabilities of the platform. None of these enterprise platforms were engineered for the purpose of authoring situational applications and so IT people find themselves knee-deep in APIs, forms and database programming projects and third party data analytics and data source connectivity tools.

Using built for purpose environments like Encanvas, situational applications are created in a codeless integrated design environment. We use the same environment to author operational analytics as we do actionable applications - and very often an exploratory application for operational analytics can be progressively extended to become a key part of the enterprise operating environment.

In the normal course of an accelerated business innovation project, stakeholders will start by creating operational analytics applications to analyze their data.  Then they will create applications to ACT on the results of the 'data analysis' exercise.

When data does not exist, new applications are authored (for desktop, tablet or mobile devices) to capture data.  When authoring such applications it make sense to control the quality of data input wherever possible by adopting the use of drop-down choice fields, radar buttons,check boxes and check-lists. When working with Encanvas, even applications used to capture data can be 'situational'.  Very often these applications are eventually absorbed by 'platform' systems like Microsoft SharePoint, Oracle and SAP.

How long does it take to execute an accelerated business innovation project from 'concept to completion?
After a decade of examples we know that the answer depends on a number of factors:

1. The size of a project - Smaller projects (i.e. Something akin to displacing the use of spreadsheets for analysis of audit and accounting processes) can be actioned within a day, sometimes two.  Large projects take significantly longer.  When we've employed accelerated business innovation to upgrade a regional transport system, enhance an eLearning system, install federated risk or compliance across a business - that sort of thing - it normally takes around 6-weeks to get through the situational applications phase and move on to User Testing or 'embedding' the technology deployment into the enterprise platform.  While 6-weeks is a long time it is nothing compared to traditional IT projects that span many months, sometimes years.

2. The change methodology - We are supporting a change process and so inevitably HOW you run a project will impact on the pace of achieving outcomes. An NDMC we've created a Computer Aided Applications Development methodology to support accelerated business innovation projects.  This combines the 'good thinking' found in Outcome Driven Innovation (ODI) and Blue Ocean strategy mapping in addition to a few other methods that are unique to our approach.

3. The tools - If you're using a codeless and complete situational applications design environment like Encanvas Design Studio then 90% of the programming, testing and tuning overhead authoring process is normally removed from the exercise - and stakeholders can contribute to workshop-based developments.  This speeds up the innovation process because project leads can achieve an outcome during the course of a 3 or 4-hour session rather than just creating 'a list of things to complete'.  If you are required to use tool-kits like Microsoft SharePoint THEN YOU CAN still achieve great results but it does mean you will need a dozen more IT people to support the process.

I hope you found this article on accelerated business innovation interesting.  Do let me know of your own experiences.

Ian.

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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What Are Actionable Insights?

This What are Actionable Insights? article introduces my presentation on 'Actionable Insights'.  In it I describe what Actionable Insights are and their relationship to the world of Business Intelligence technology.
You can go straight to the presentation itself on http://www.slideshare.net/ictomlin/what-are-actionable-insights..and if you'd like the PowerPoint original just reach out to me on LinkedIn!

Understanding what Actionable Insights are and their contribution to business growth is something I’ve always been interested in.  Having produced a couple of posts on the subject I’ve had such a high level of interest in the topic that I’ve produced this simple presentation that hopefully demystifies the topic to newcomers and non-technical folk.

BUSINESS INTELLIGENCE has been around for many years now and yet I still attend meetings where people don’t really understand what it does to help them to grow their business or get their job done better.  And that’s not surprising.  It’s such a broad topic and BUSINESS INTELLIGENCE SOFTWARE doesn’t do just one thing.  I find it much easier to break down BUSINESS INTELLIGENCE into the various capability areas it covers.

OPERATIONAL ANALYTICS is one facet of BUSINESS INTELLIGENCE SOFTWARE – but it’s growing in importance because of the level of interest and opportunity that’s been surfaced by BIG DATA and CLOUD COMPUTING.  I think people will automatically assume that you need new software for operational analytics but even a spreadsheet can be used to source Actionable Insights: The secret is to focus on the business outcome, not the tools (quite so much;-).

I.