Making Your Data Customer Centric

By Sid Adelman

How Data-Driven Marketing Can Fix A Broken Business Model

Your customer interactions are increasingly affecting your brand.  As a marketer, what kind of control do you have over these touchpoints and should you allocate marketing funds to improve the customer experience? Your company as a whole needs to take a step back and (re)evaluate its measures of success.  Considering your customers’ newfound leverage, Lifetime value (LTV) should be at the top of the list.

LTV is negatively affected when unqualified leads are brought in, or when new customers are too aggressively cross-sold to, or when e-newsletters are no longer opened due to irrelevancy, or when customer service has time or revenue quotas, or when the lease-end communications have gaps and conflict with one another, or when… fill in the blanks for your company here.

LTV is positively affected when the company simply gets in their customers’ shoes.  Each interaction should be a value exchange that solidifies the relationship, instead of a self-serving solicitation or irrelevant message.  Not only do they start tuning you out, your brand value plummets when marketing to the lowest common denominator disenfranchises the rest.

Customer data is key to this process and there are a number of types of customer data.

  1. Account Information – This is the type that’s most familiar to us. It includes your account number (frequent flyer number, hotel loyalty card number, checking account number, patient id, casino frequent loser number), name, postal address, email address, home phone number, work phone, fax number, social security number, along with the type of service provided by the institution, e.g., checking account, insurance policy, type of cable service.
  2. Relationship Information – This information is usually associated with householding where we understand the customer’s relationship to other members of the household including spouses, parents, children, significant others, partners, but can also include business associates and close friends. A danger here is old data that might cause a reference to a divorced spouse especially when viewed by a current wife or girlfriend. This points to the necessity of the data being reasonably current.
  3. Customer Preference Information – The airlines know you want an aisle seat; the hotels know you want a non-smoking room with hypoallergenic pillows, the car rental company knows you want a sporty car, non-profits know you don’t want to be solicited by phone and some resorts know you don’t want a letter thanking your spouse for joining him or her on that trip to Vegas. A customer’s preference for flying Business or First Class on an airline will probably indicate a higher LTV.
  4. Demographic Information – This is data you provided or gathered through subscriptions, warranty data, loyalty cards, web sites, and other sources that can be purchased from third party suppliers. This could include marital status, education level, household income, number of children, hobbies, interests, age, and credit score.
  5. Service Information – This would include your interactions with the institution. A non-profit would capture how much you’ve donated, when the donation was made, any designations for the donation such as the new wing on the hospital, athletic program, and bonuses for the dean. It would also include information on your phone calls, letter, and email correspondence including complaints. The phone interactions could be captured by the customer support representatives with text and with classifications for the calls. Data obtained from customer service could be a trigger for a targeted solicitation.
  6. Billing Information – This data would have the invoice, payment, and dunning data as well as the status of interactions related to billing and changes in the service provided by the institution.
  7. Promotions – This data has the history of promotions and solicitations to this customer and the responses to those promotions. The book stores have coupons, the cruise lines have off-season packages, and the airlines have limited low-cost fares to Duluth. Direct marketers know which catalogs were sent, and what was purchased from those catalogs.
  8. Purchasing/Service Information – This includes what the customer has purchased from us, when, returns, mode of payment, and problems with the purchase. Amazon knows what books you like and is right there with suggestions for additional reading material. Purchasing information can be another trigger for an offer geared to the customer’s interests.
  9. Immediate Action Information – This could include data related to homeland security, a no-fly list, wanted criminals, child abductions, flight risks (people on bail), potential terrorist, infectious exposures, all where immediate action might be required.


There are interesting problems with all this data including where and how it is stored, how long the data is kept – for example do we keep data on customers who are deceased and for how long? Note: Deceased customers have a dismal record of buying our products but for regulatory and legal reasons and for certain industries (banking, insurance, financial) maintaining these records is a requirement.

Another problem is the ownership of this data. Who will be making the decisions on the retention, security, data quality, availability, and performance requirements of the data? Clearly, decisions must be made and they should be in conformance to the data standards of the organization, assuming such standards exist. If they don’t exist, the organization is not husbanding one of its most important assets, its customer data. While Operations has responsibility for completing a transaction, Marketing is the part of the organization where the sale is made and Marketing should therefore be the data’s owner. We recognize that this recommendation is highly controversial and will not sit well with many outside of Marketing. However, we have found that if Marketing is not the owner, the data will rarely be effectively used.

A difficult technical and business challenge is the integration of the data. The ideal is a 360 degree view of the customer’s data that would allow a comprehensive view of the customer that would allow both automated and human appropriate action to be taken based on this holistic view and understanding of who the customer is, their preferences, interests, relationships, and their LTV.

Data should be integrated to feed two distinct areas: triggers and clusters. The triggers should be as real-time as operations and cost allow. Organizations should also focus on leveraging key touchpoints in real time and should implement dynamic placement of customers in needs-based clusters to better match content to their needs.

Triggers are simply those customer events that are inherent to the sales cycle, like opt-in, purchase, support call, service call, anniversary, end of lease and defection.  As mentioned above, other functional areas often own these triggers and have put their own touchpoints in place, resulting in gaps, inconsistent branding and even conflicts from the customer’s point of view.  CEO buy-in is needed to provide Marketing ownership and control at which point a series of consistent and synergistic communications can be designed and, along with pre-defined business rules, permanently put into place with the aid of marketing automation technology.

Between implicit behavior, explicit profiling and external appends, the data integration effort also provides the intelligence needed to properly identify needs-based clusters.  These can play a role in the above triggered communications, but are of most value in the allocation of content from a variety of sources.  Specifically, instead of a one-size-fits-all newsletter you can have a dedicated editor distribute content “snippets” to the most appropriate cluster(s).  These snippets, similar to text-based alerts, are derived from a host of sources from within and outside of the company… as long as they are relevant to the recipient/cluster.

Ok, so you may wonder, I have these automatically triggered communications churning away in the background, I have an editor distributing all kinds of interesting content to the clusters… but I need to sell NOW.  Of course you can still send a single offer to the full opt-in list, but why would you when you can leverage the channels already in place with better results?  Simply integrate the more generic offers into the triggered communications stream, and come up with cluster-specific variants that are sent by the editor. Such an infrastructure can free up Marketing to focus more on being proactive about the ever-changing marketplace, instead of always being bogged down in operations.

After integration and analysis, the third leg of data is measurement.  As mentioned, LTV is the perfect measure in a customer-controlled environment, but unfortunately this statistic isn’t final until the end of a customer’s tenure.

Restraint is then important, as data-driven marketing’s success cannot simply be measured with a single response rate.  By organizing your marketing teams around lifetime ownership of the customers they acquire, they will be much better aligned with long-term profitability and a healthier brand.  Unfortunately, companies often have separate acquisition and retention departments, each with conflicting KPIs, resulting in “churn & burn” reputations and subsequently a tarnished brand.

More than ever, your business model is wedded to your customer base.  Marketing needs to become more customer-centric and data-driven to properly respond and even thrive.  But this will require investment in downstream communications and infrastructure, and patience is needed when it comes to ROI.  But in our book, the peace of mind that customer loyalty brings is better than any sleeping aid.

About the Author

Sid Adelman is a principal consultant with Sid Adelman & Associates, an organization specializing in planning and implementing data warehouses, performing data warehouse and BI assessments, and in establishing effective data strategies. He is a regular speaker at “The Data Warehouse Institute” and IBM’s “DB2 and Data Warehouse Conference”. Sid chairs the “Ask the Experts” column on, and has had a bi-monthly column in DMReview. He is a frequent contributor to journals that focus on data warehousing.  He co-authored one of the initial works in data warehousing, Data Warehousing, Practical Advice from the Experts, and is co-author of Data Warehouse Project Management with Larissa Moss. He is the principal author of Impossible Data Warehouse Situations with Solutions from the Experts and his newest book, Data Strategy, was co-authored by Larissa Moss and Majid Abai. He can be reached at 818 783 9634and [email protected]. His web site is

Free Expert Consultation