Grading Performance – The Automotive Industry BI Maturity Map

Automotive companies are increasingly placing emphasis on becoming customer centric, investing significant time and resources towards this endeavor. The effective utilization of business intelligence (BI) in any CRM enhancement effort is a must, necessitating that automotive companies assess where they are, as well as plan where they want to be, around this field…


The investment by automotive companies into CRM (be it people or software, IT systems or loyalty programs) is on the up and up. Few (inside or outside the companies) would in general consider these investments “effective” to date; for every successful CRM initiative launched by an automotive company, there are dozens of apparent or hidden failures.

Behind most effective CRM efforts are people, analytical enough to understand the customer base, smart enough to capitalize on this understanding. At the very heart of most of these efforts is business intelligence (BI), data that through analysis allows for opportunities to be identified and acted upon. In order to assure and maximize the value add of CRM efforts, automotive companies should take a broader look at their BI capabilities, to identify if and how improvements need to be made. Without effective BI practices in place, many a CRM effort is doomed to fail, being guided not by data but by gut feel. To that end, we recommend companies assess how mature their practices are across a variety of BI areas.


Assessing the maturity of BI related efforts can help automotive companies in various ways:

  • To ensure there is a clear definition of the baseline around company BI capabilities.
  • To identify the current problem / improvement areas.
  • To define paths in which employees and departments need to be aligned around to move forward.
  • To define a list of milestones which can then be turned into targets.
  • To set an objective scale which can be used in benchmarking.


The “Automotive Industry BI Maturity Map” defines four different maturity levels that can be applied against five different areas under BI:

BI Maturity

Maturity Level 1: Barely Basic

The “Barely Basic” level is the first stage of the business intelligence journey. At this level, the company has done almost nothing around developing its business intelligence efforts, nor around using data to take actions. Companies at this level are usually here due to lack of awareness about the potential benefits of BI, or absence of the mandatory enablers (such as capable workforce or proper data systems).

Data Management at this level is one in which companies have started to log sales and service data, but not always as related to each other. Basic demographic information (such as age or gender) is logged but usually significant data quality issues exist.  Automobile uniqueness is usually in place but customer uniqueness is questionable. Reference values required to give meaning to the records (such as car configuration or accessories reference table) are usually not up to date.

Technology at this level around supporting business intelligence efforts is none to minimal. Technologies are selected and managed according to current employee skill-sets, with technology investment decisions and budgets solely owned by IT. MS Office programs are used as the main platform for information exchange within the company.  There is no data mining tool in place or one being considered as of yet.

Reporting at this level is such that there is almost no reporting beyond that done for tracking basic financials. Ad-hoc reports are provided up to a certain degree, but consistency and efficiency are always in debate. Reports are not highly valued by management, thus little demand exists within the company for them.

Analytics is bare bones at this level, with little to no modeling in place, as well as no capabilities to conduct modeling. At the most there is basic segmentation around customer value, likely tied to the model of the automobile, or to total revenue generated by a customer over a given amount of time. This is significantly driven by the fact that there is minimal to no demand from the business units (marketing or sales) for customer analytics solutions.

Governance of BI is as well practically non-existent; BI-related efforts are managed haphazardly, with no formal business unit in place to oversee and handle them. Roles and responsibilities as such are not defined either, with no employees dedicated in a full-time manner to handle business intelligence-related tasks. Basic concepts have been defined (i.e. customer churn, profit margin, etc.), but are rarely relied on or utilized.

Maturity Level 2: On the Way

Companies at the “On the Way” level are those which realize the importance of BI and have started to tap into its benefits; they have yet to, however, overcome design related issues (i.e. data management structure design, analytical models design, roles / responsibilities design, etc.), or scale up BI operations such that it is sustainable on its own as a business unit.

Data Management at this level is one in which data is stored in a proper manner (i.e. in a data warehouse), though not in a complete manner (i.e. lacking prospect data). Data is both deeper (i.e. not just customer name, age, and address, but also occupation, education level, etc.), and richer (i.e. not just dealer visit information but also contact center interactions). Customer uniqueness is also assured, with a unified view of all customer sales and service data in place. Information can be historically tracked (such as a customer’s service pattern changes with a demographic stage change) as well. Data inventory and data quality is monitored regularly, but usually only from a company perspective.

Technology at this level is more advanced, with basic reporting tools in place, possibly a data mining tool as well (though not being used effectively or in an ongoing systematic manner). Alternative technologies for the main architectural components are known and reviewed regularly. Vendor evaluation scorecards are in place and SLA management structures have been established between the company and vendors.

Reporting at this level is automated though standard and basic, with monthly reports issued around common and key KPIs, distributed across business units (with some customization at the business unit level).  One view of truth has been assured (such that all reported data has been reviewed and is free of discrepancies and inaccuracies), with ad-hoc reporting also in play but at a minimal level.

Customer Analytics is more advanced at this level, such that customer differentiation beyond value is in place (i.e. differentiation based on service usage behavior). Other basic models have been developed as well (such as churn prediction) but are not in use on a common basis; performance and accuracy of the models is questionable.

Governance has improved significantly at this point, with the establishment of a formal BI function within the company. BI personnel have assigned roles and responsibilities, with marketing and sales interaction common and frequent (though not systematic). Ad-hoc analysis are performed more frequently, with both BI and business units aware now of the value of tapping into data.  A data dictionary has been developed to serve as a reference for all stakeholders, with the entire set of customer definitions in place (i.e. “active service customer,” “sports car savvy,” etc.).

Maturity Level 3: Ahead of the Curve

Automotive companies that have made it to this level along the BI evolution journey have fully embraced the concept of tapping into data, viewing it as a main competitive advantage. Data is viewed as a critical asset, assessed constantly for completeness and accuracy, compiled and shared across all parts of the business.

Data Management at this level is one in which all data-related processes, measurement methods, controls, etc., are working smoothly. There exists a proper data strategy (which data to collect, where to collect, when to collect) at this point that is adhered to and realized. Data is comprehensively collected across internal channels (i.e. even around prospective customers), with data quality controls in place across all channels. Data privacy principles and guidelines are known and properly documented.

Technology is being tapped into and leveraged significantly at this level, with data mining and reporting tools used in an advanced manner. Experts around using the tools reside within the BI unit, with power users as well in the marketing and sales departments. The company is at the point where they are examining alternative solutions rather than a particular technology (with outsourcing, open source, or cloud solution options being reviewed and evaluated regularly). Demand and efficiency are headed in different directions, with requests from the BI unit constantly increasing, the turnaround time needed to fulfill requests constantly decreasing.

Reporting is fully automated at this level, customized for the various stakeholder groups in the company. More advanced reporting concepts (i.e. dashboards, scorecards, etc.) are in play, with KPIs being reported on a daily, even a real-time basis in some cases. Business units are power users as well, able to develop their own ad-hoc reports. Visual and graphic representations are fully utilized in reporting to allow for an increased ease of interpretation.

Analytics is in overdrive at this level, with models developed across a variety of spectrums allowing for strategies to be designed and carried out around acquiring, growing, or retaining the customer base. Segment management has been fully enabled, with an understanding of prospects and customers in and around their value, needs, and behaviors. Predictive sales propensity models have been developed and are being used to proactively re-sell to existing customers. Stock levels have been optimized thanks to sales / demand forecast models. Accuracy of the models is a given, as is automation; models are re-running each month with little to no manual support needed.

Governance at this level as well is almost an afterthought, with everything in and around BI running like a well-oiled machine. BI and business units clearly know their roles and responsibilities and adhere to them. KPIs are understood and adhered to. Collaboration between all stakeholders is significant, with all parties seeking to constantly improve BI-related performance and outputs. The BI unit is viewed by business units and executive management as a peer and not a support unit.

Maturity Level 4: Best-in-Class

The few automotive companies that have made it to this level are clear leaders in and around the BI domain. Most of the key decisions being made in such companies are not only being triggered by information, but also validated or dispelled by it. The utilization of intelligence in all activities across all business units is essentially a KPI, expected of all employees, reflected in their roles and responsibilities. Improvement opportunities for bettering BI-related efforts are almost at a minimal.

Data Management at this level is an after-thought; data is being collected in an accurate and complete manner from not just internal sources but from those interfacing with external parties as well (i.e. social media interactions, web behaviors, etc., are being tracked and logged). There is nary an interaction or event in and around the automotive company that is not monitored, noted, and stored in the data warehouse; data is deep (with records going back at least ten years), data is accurate (error-free), data is granular (down to the hour, down to the conversation details level).

Technology is being used in an efficient manner as is possible, with all hardware, software, processes, etc., optimized around the company’s BI practices. Certain efforts have been outsourced, allowing for the company to internally focus on value-added activities only. Almost everything works in a real-time manner, allowing for decisions to be made on the fly. There are essentially no opportunities for the company to improve how data is stored, accessed, manipulated, and disseminated; the company is, however, always on the lookout for such opportunities should they be made available thanks to new developments in technologies.

Reporting systems at this level are proactive; alerts have been built into reporting systems, notifying interested stakeholders in a real-time manner when and if needed (i.e. an email sent to a District Sales Manager when stock levels of a certain model hit a critical amount, ensuring action is taken immediately to drive replenishment). Standard reports are being updated in a real-time manner, as real-time as the data flowing into the company. They can be accessed now through mobile solutions (i.e. tablets and mobile phones). Tailored reports are produced and shared as well at this point with 3rdparties (i.e. dealers and suppliers). Report usage intensity, diversity, and frequency is monitored to understand reports’ fit-for-need, with modifications made if needed to drive uptake.

Analytics is extremely advanced, with concepts like customer lifetime value being analyzed and modeled to allow for longer-term strategies to be deployed on a customer by customer level. Model outputs are being used across all channels to drive up the retention and growth of company value in a systematic and automated manner. Additional advanced concepts like GIS based analyses are also being conducted (i.e. potential analysis, new dealer or service location / site selection).

Governance at this level is a concept that is not needed or thought of on a regular basis. Stakeholders in and around the BI unit operate in an efficient, optimal, and ideal manner, requiring no procedures or policies be followed (as the operation flows completely smoothly).  The company culture revolves around the utilization of data in decision making; as such, the BI unit is at the heart of all company efforts.

What Next?


Once automotive companies identify what level they are at in each of the BI areas, a plan / strategy should be designed for moving up one or more levels in those areas the companies feel they need to be stronger in. With tangible targets defined, a BI development roadmap should then be designed, with the targets and relevant deadlines assigned to various stakeholders. Monitoring of the evolution across the areas then should be a common practice to ensure targets are being hit.

Tags : analytics, automotive, business intelligence, consulting, customer analytics, customer segmentation, data mining, intelligence, reporting, segmentation