Pushing the Envelope: How Custom AI turned Neighbourhood Mail into a Profit Machine

Every marketer must make a decision as to how much to spend on each channel of communication available to them.  In our experience, budget goes to…and stays with…channels that consistently deliver measurable results at a par or better than any other channel available.  But many marketing teams today are overworked and understaffed.  They need channels that are easy to plan and buy, delivering those results with less risk and less effort than competing alternatives. 

Digital platforms have done an excellent job at making it easy to plan and buy.  (delivering profitable results?  not so clear).  This post will focus on how a channel that has been with us for a very long time…Neighbourhood mail…was re-invented by adding custom AI tools, to become a profit machine. 

We will look at a case involving a telecommunications company, selling Home Phone, Internet Service, Mobile and Bundles (combinations of services at attractive prices).  The company’s competitive advantage is price; they offer services at a 20-30% discount to national major brands at comparable quality. 

Before using Neighbourhood Mail, the company relied on digital campaigns, especially search, to sign up new customers.  While the success rate of such work was acceptable, volumes were not. Neighbourhood mail was considered as a channel that could deliver reach to all serviceable households in their footprint, and sign up customers at an attractive cost. 

Often Neighbourhood Mail is targeted by demography or region, and used as a media to reach a lot of households.  But for this case, such an approach yields poor results; more precision in targeting was needed to generate a good result. 

To support this effort, Navigation ME built a set of models, each of which predicted, for a given time period, the activation rate (number of new customers divided by pieces mailed) for a given postal walk (the unit of NM delivery) for a given product. 

In building these models, we found that many factors affected activation rate: 

  • How many times we had sent mail about a given product to that postal area, and how recently (or how many times we had sent mail about another product) 
  • Impact of other channels used in the same areas at the same time 
  • Demography; including income, family structure, and age distribution 
  • The baseline of demand for a given product 
  • Pricing vs competitive alternatives 
  • The penetration of current customers in that area, and the trend; was new customer acquisition outpacing churn, growing penetration?  How quickly?  Or was it the reverse; we are having trouble keeping customers in that area and replacing them when they go 

The difference in predicted activation rate, and therefore cost-per-activation, was large; top deciles were often at 4x as high an activation rate as the 5th decile; and the bottom deciles typically delivered hardly any activations at all.  (note; the measurement of activations took into account multi-channel effects, isolating the impact of Neighbourhood Mail for better decision-making).  Model accuracy rates, from pre-campaign scoring to post-campaign evaluation, are consistently in the 92-95% range. 

The cost of delivering a given service depended on its footprint.  This meant that since we wanted to calculate the predicted profit of mailing a given postal area, we needed to know the cost of delivery in that area. 

We found that churn was not uniform in all areas; there was large variation with some neighbourhoods consistently churning at higher rates.  We needed to take this into account as well, since the value of a new customer from those areas would, all other things being equal, be less than in areas that showed lower churn risk. 

For each postal walk, we had 7 scores; one for each product/footprint combination. For a given postal walk, the score for one month would be different depending on whether we mailed the previous month or not. 

To avoid this level of complexity from overwhelming decision makers, we added our custom optimization algorithm, tasked to find the optimal distribution of mail: 

  • For each postal walk… 
  • …for each of the three months of the campaign… 
  • …for each product… 

This meant some postal walks could get Product A for months 1 and 2, and Product B for month 3.  Or a given walk might not get any mail at all, if the model predicted the costs would exceed the profits of the predicted number of new activations. 

Interestingly, about 1/3 of new activations came from existing customers, something the model picked up by taking penetration momentum into account.  So in this case, Neighbourhood Mail became not only a profitable new customer acquisition channel, but a good cross-sell channel as well. 

For planning purposes, we created an ROI curve: 

Each point on the curve was the result of the model doing an optimal allocation of available budget across waves, time, and product.  All the work of taking full advantage of the model’s learning and accuracy was done for the planners by the software. 

Since there is no free lunch, the curve flattens as more pieces are added.  This reflects what we know about all advertising; after a given point, additional touches add nothing to business outcomes.   

The ROI curve enables a planner to increase spend with confidence of the outcome (risk can be calculated explicitly for each scenario).  It also demonstrates the overall impact of this channel; for a mailing of 2.5 million pieces, for example, a profit after all variable costs and campaign fixed costs are paid of over $4.5 million is generated. 

We have been running this system for this client for many years; with results in line with model expectations each time.  Predicted vs actual is carefully tracked by decile so we know the model continues to track today’s market conditions. 

Taken together, the reach of Neighbourhood Mail plus the decision-making impact of Navigation ME’s predictive and optimization models has created a profit machine…high performance, with consistent results, at low risk and that is easy to plan for.   

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