There is no need to emphasize the increasing criticality of customers. The trend has moved from customer focused to customer obsessed. In todays economy, staying competitive and profitable and providing the best customer service are the top goals for many organizations, and knowing your customers and prospects through the right information is critical to the attainment of all these goals. With customer insight, the organization can get a leg up on winning over the customer by attracting new customers, expanding business with existing customers, establishing relationships with good customers and providing top-notch customer service.
Information versus Insight
What is customer information? What is insight about customers? Information is what the business lines can use at the right time, at the right location and via the right media at their discretion. When a customer contacts the call center, the representative should already know that he/she just had a baby, so the customer might want to consider a college fund account. When the salesperson walks into the clients office, he has the clients current information handy, and when the client asks an impromptu question about his past financial activities or claims, the salesperson should be able to immediately pull out those activities on his laptop. When marketing needs some aggregated customer information to present to the executive, a well-formatted report with accurate data can be produced in a timely fashion instead of months later.
Then what is customer insight? Insight is more than just customer information. Its about knowing the customers and providing customer intelligence entailing customer trend analysis, customer pattern discovery, customer demographic analysis and customer behavior forecasting. It also triggers alerts when good customers are shrinking to a certain level and send out congratulations alerts when good customers are increasing.
Challenges of Knowing the Customers
Even though a lot of effort has been put into collecting, storing and cleaning customer data, it is still a growing challenge to get customer insight - knowing what they need at which phases of their lives and their changing needs along with their changing businesses.
The challenge does not come from the lack of the data - the customers demographic data is provided to us at every occasion: phone calls to a call center, follow-up with sales, application for an account, inquiry about products and their various transactions. Very often, we lose a good customer who claims that he goes away because we dont offer all the products he wants. The inquiry data is there in the database, showing this customer has called multiple times about his interest in other products with the expansion of his business. But why have our marketing or sales or product lines not detected this data? The reason is very simple - because it is just data, and it is not relevant, organized information that provides insight for decision-making.
Businesspeople would applaud if such information could be pushed over to them or easily found through simple clicks. If they can easily have insight about their customers, they can spend their time more profitably. But the reality is that multiple challenges exist:
- Insufficient executive sponsorship. Usually a marketing executive spearheads the customer data initiative for marketing campaigns and there is little support and involvement from other departments. Instead of being enterprisewide, the effort becomes departmental, defeating the purpose of being beneficial across the organization.
- Low customer data quality, including incomplete data, duplicate data and obsolete data. Addresses may be missing ZIP codes, which are critical for demographic analysis; one customer may have moved but the old address that is still in the record; another customer may have changed her last name after getting married, but both records exist in the databases as the current names. These data problems inevitably prevent customer insight.
- Unsystematically organized historical customer data. We have a sizable amount of historic customer data spread out over different databases which take up a lot of disk space. It is not an easy job when we want to forecast how customers will react based on their past performances. The reason is pretty simple: the historic customer data is not well organized and not user-friendly.
- Lack of an integrated customer, household and relationship intelligence and lack of an integrated view of customers and products. Customer data by itself may give some demographic information, but adding the relationship to an individual customer will shed much more light on them. Household information provides more opportunities to offer better service. Relationship intelligence can help with the up-selling and the combination of customers and products will benefit cross-selling. Somehow those are often missed in the scope of customer data initiative.
With all these challenges, how about the current technologies such as customer data integration (CDI) and enterprise data warehousing, and can they be the solution to the customer data challenges? CDI was originated from customer relationship management (CRM) and, as a part of the CRM system, it is a good operational source of customers. But it cannot meet the needs of comprehensive customer analysis and customer trend and pattern detection, because it lacks all other information such as products, transactions and accounts. An enterprise data warehouse should achieve the integrated view of customers and fulfill the analysis requirements. But the EDW is a big initiative, which encompasses all lines of data from the entire organization. With its scope, it asks for lots of time and sizable capital for funding. Moreover, it has to accommodate a wide spectrum of data structures from different data sources and to include all the required history information. And it does not focus on customer information at all. In todays rapid-changing market, to stay ahead of competition, technology in the organization is quickly shifting as well. A big bang of an EDW solutions with a very broad scope is ideal to have but hard to roll out at the enterprise level scale in the time frame mandated by fast-paced business.
Eight Tips to Provide Customer Insight to Business
Develop customized customer data marts. Experience of my implementations illustrates that building customized customer data marts in a common framework provides the business insight on targeted customers while focusing on customer data and is feasible to implement overcoming the problem of failure to keep up with the fast-changing business with a big bang solution. In this framework, customer performance, product performance, their associated relationships, transactions and accounts will all be based on consistent governance of collecting, retrieving, defining, cleansing and transforming data across disparate systems. This data is then fed into the various customer data marts with different data models for a variety of business lines, turning data into information, then into true customer intelligence.
Put customer data quality as the first priority. Knowing customers means having the right data about customers based on the correct information. Customer patterns, good customers, cross-sell opportunities and trending can be identified. These analyses rely on availability and accessibility of the accurate customer and prospect data. The customer data mart projects need to have a data quality initiative as the first step, which ensures that the data coming from the source is right and the incorrect data is cleansed. Both the upstream and downstream processing need to set up clean-up mechanisms. Without good data to start with further development and delivery is impossible.
Build a common customer framework as the infrastructure. A common customer framework incorporates everything about a customer or prospect. That is the source for all the customer data marts. To put a solid common customer framework in place, marketing, sales, product development, finance, call center and all other units that work with customers need to collaborate closely, and the old departmental walls need to be broken down. Only with information flowing through departments without major blocks, can the framework cover the entire organization and serve as the infrastructure for gaining customer insight.
Lay out the roadmap of the deliverables. Pushing customer insight to the business users is a relatively big effort and spans over a period of time and a series of phases. The best practice is to lay out the roadmap of deliverables and present it to the business users. Business users will be delighted to know what functionalities will be included in each deliverable. A lot of times, the failure of a project is attributed to the ambiguous definition of what will be implemented for the project. Therefore, the clearly defined roadmap paints a clear picture of when users will get each customer data mart, and it will help users buy-in and managing user expectations.
Develop customer-specific agendas in the data governance council. Data governance council is a useful platform where data issues get resolved and decisions are made based on anything related to enterprise data. In the council, the topics range from data dictionary to metadata definition and cover all data areas. With such a broad range and a wide variety of data, it is sometimes very hard to focus on one agenda. In order to nail down the challenges regarding customer data and resolve any issue relevant to customer analysis, specific agendas on customer information need to be developed in the council. The agenda should specify the topics to be discussed and issues to be resolved, and it will help with decisions to be made regarding the customer data initiative.
Involve business all the way through. Business sponsorship is critical to the success of building customer data marts. It is well known that without the involvement of the business, customer data marts will have little chance of success. But the business involvement has to be all the way through, starting from executive sponsorship and endorsement, cooperation on the requirements and frequent updates of both business and technology changes. Regular Q&A sessions should take place between the two in the process of project development to SMEs reviews and user testing.
Involve all customer-facing business units. Many business units have a requirement for doing customer analysis, and different units look at customer data from different perspectives and use the data differently as well. So, the voice of all the business units who work with customers or customer information needs to be heard - marketing, sales, finance, product strategy and call center. How about IT? IT doesnt seem to work directly with customers, but IT supports all the units that do, and IT maintains the databases that store customers information, so IT plays an important role in both technical and business areas.
Integrate customer intelligence and product intelligence. Customer intelligence by itself can reveal some information, but the combination of customer and product intelligence will reveal more insights. The integration of the two will illustrate what existing products are customers most preferred, what they would like to have, what they like about the products, and what product line fits into various demographic customers. Combining customer intelligence and product intelligence will provide business value to more than one business unit.
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