Offer Management Stages Part 2
Beyond the land of unqualified and risk-qualified offers lie the far more sophisticated and effective potentials of targeted and personalized offer management.
Stage Three – Targeted Offer Management
Stage Three is where risk is combined with additional data to build a more robust profile of the customer. For example, an understanding of personal income moves the conversation from a “can a consumer have this product” to “should a consumer have this product.” Not everyone in a risk profile is in the same financial situation. A perfect risk score and no free income indicates a waste of marketing spend and the potential aggravation of a happy DDA customer.
Differentiated assets and the corresponding analytics are the keys to seeing serious value in this space. Getting away from risk and standard-demographic data leads to the greatest value in data assets. Statisticians who know how to blend the assets pull the value from the numbers. Ultimately Stage Three is part of the journey, not the destination. Segmenting your customer base by best-offer is helpful, but once the initial “gain” is achieved, it is tough to get better. If you offer a product to everyone with an available income of $10,000 and a middle risk score today with a 5% acceptance, you will likely see the same results the next day. Until a policy starts understanding “why” an offer wasn’t accepted, offers are the only solution.
Solutions that provide the next best actions are what drive behavioral changes and increases in model results. Good segmentation doesn’t require channel integration. While the better personas equate to better offers, there is still the question of whether a customer is looking to expand his or her relationship. Ultimately segmenting “propensity to buy” needs a close understanding of the buyer’s behavior, something that defines Stage Four.
Stage Four – Personalized Offer Management
Stage Four is where an offer policy enters continual learning. By combining the segmentation of external data with the behavioral clues of internal behavior, retail banks can move past just making offers. Internal data allows for a personalized action-based system. If a customer uses a branch only to deposit the occasional personal check, it may not make sense to leverage these interactions for new offers. If customers have spent time looking at available cards at your site, it might be wise to try to get them on the phone or reach out via e-mail to let them know what products they might be interested in, based on their browsing history. For example, offering what others in their life stage have found useful, and what next steps might be. Stage Three leaves you driving the relationship based on corporate profit desires. Stage Four drives campaigns based on needs and wants of customers.
Banks can leverage a variety of tools to assist in making this vision a reality, but moving to Stage Four requires a “holistic view” most banks lack. Internal IT groups usually are not structured for the end-to-end accountability required. Traditional providers in the space may know the technology, have an armada of statisticians, but are too data-centric and force banks to pick two of the three. Getting to Stage Four requires all three.
Aite’s recent report on online marketing indicates retail banking isn’t really in Stage Four yet. The perpetual ad-fatigue customers are getting at every interaction doesn’t indicate a strong Stage Three presence. With tighter margins from growing fees, customer-centric banking is the buzzword. Until banks are marketing with strong behavioral triggers that get optimized by back-end algorithms on a regular basis, growth will remain elusive. Getting to this point is a strategic and technological process and Equifax recommends partnering with technology and analytic providers who can see the path and help your institution walk towards a targeted, best action approach.
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