The optimism of #Purpose & science of #design in forging #digital customer experiences! Opinions my own!

Direct marketing is a balancing act: approaching our customers multiple times in a short timeframe we could either improve our results or experience list fatigue by contacting the same audience one time too many. List fatigue may lead to lower response rates and marketing yields, but worse, customers may opt-out altogether. It’s a worrying trend, and all marketers I feel have a responsibility to enforce contact governance; failing that the whole industry suffers, not just ourselves.

Case in point, a non contactable customer is revenue lost, short and long term. Estimates in financial services put the figure at US$30 per customer, and that’s not including future revenue streams from orchestrated interventions in their lifecycles.

Having a contact strategy is therefore vital, but what is an optimal contact policy? Do contact strategies vary by customer and segments, and what of contact hierarchies? Finding the right balance can be challenging because the best approach is different for every organization, and within those organizations, contact policies (should) change.

Fortunately, we can optimize our contact strategy­­and marketing returns­­ by taking a look at our target selection criteria. Optimizing contact strategy is particularly critical for direct marketers in industries such as telecommunications and financial services that due to highly competitive market conditions, heavily target their prospect and customer franchises with a large number of direct marketing interactions within a short time.

In determining the best balance in our contact strategy, consider the following variables:

Contact frequency – This is the number of times a customer will be targeted within a specific timeframe to drive the maximum response rate.

Contact recency and quarantine (no contact) period – Together, these determine the optimal time window for non permissible outbound contacts to drive the highest response rate. Marketers must find the length of time that a target audience must be quarantined from contacts in order to avoid response degradation – else they opt-out, or worse, place themselves in a national Do-Not-Contact registry.

Creative and offer rotation – A fine line exists between building awareness and boring a prospect with the same creative or offer. Marketers must determine the optimal rotation sequence to drive the maximum response rates from a prospect.

Contact hierarchies – Put it another way, we don’t want to be sending incessant sales messages nor do we want to be overly focused on customer service – somehow a balance must be struck.

Customer lifecycles and life-stages – Some companies have adopted onboarding programs, a set of planned interactions spanning say 45 days for customers once they join. These help us to categorize contacts by relevance.

But where do we begin? Perhaps start by following these recommendations:

Institutional marketing memory  – store contact and response histories in a centralized campaign data mart

Access to relevant data is vital. Invest in a campaign data mart to store the details of every campaign, every interaction and every offer made (accepted or rejected by the customer). These contact records are natural byproducts of a campaign management system.

A contact strategy that includes our business rules related to contact frequency, quarantine period, and creative/offer rotation. Create a continuous testing environment to test various dimensions of our contact strategy. Use multivariate testing techniques to maximize what we learn from our test designs.

If we plot response rates against the number of effective contacts, we should see a plateau. The type of contact matters, as would the offer and timing of the contact; all factors we must account for.

Repetitive multivariate testing through randomized tests is usually the best method to get to the best answer.

Use contact history data as a predictor in your response models. This is called adaptive contact planning and contacts should be optimized at a customer level. With campaign histories as an input we are better able to determine the equilibrium between response rates, revenue potential and constraints such as a budget we have to work with.

There is merit in adopting a data-driven approach to optimizing our contact strategies. This way the marketing we do creates sustainable value, not just short-term gains. It’s useful to know that while every company wants to be customer-centric, most of our balance sheets are still quarterly driven!