BUSI 442 Week 6 Discussion Board Forum 6 Example

Discussion Board Forum 6: Retention Priorities

BUSI 442 Customer Retention Through Digital Marketing, Discussion Board Forum 6 Thread and Replies

Thread by [Student Name]

The forum prompt and the two classmates are composites; the textbook, the research and the Scripture cited are real.

Thread

Most businesses divide their marketing budget by habit. Acquisition gets the largest share because new customers are visible and easy to count, retention gets what is left, and lapsed customers get almost nothing. Roberts and Zahay (2018) describe customer relationship development as a sequence of stages, from acquiring customers through retaining and growing them to winning back those who leave, and they emphasize that each stage requires different tactics and data. I want to argue that the budget should follow the same logic: each group of customers should receive spending in proportion to the value that spending creates, not in proportion to habit.

The customer equity framework makes that comparison possible. Rust et al. (2004) proposed treating the total discounted lifetime value of a firm's customers as the measure marketing should improve, and they showed how firms can estimate the return on investments that change customers' views of value, brand and relationship. Once every program is measured by the customer equity it creates per dollar, acquisition, retention and win-back compete on equal terms.

Consider a small online pet food retailer with 20,000 active customers, 6,000 lapsed customers who have not ordered in a year, and a monthly budget of $15,000. Acquiring a new customer through paid social ads costs about $45, and a new customer is worth about $60 in margin over the following year, so acquisition barely pays in the first year. Keeping a current customer who might otherwise leave, through a reorder reminder and a small loyalty reward, costs perhaps $4 per customer reached, and each customer kept is worth over $100 a year, because pet food customers reorder monthly. Win-back is in between: a series of three emails and a modest offer to lapsed customers costs about $2 each, and if 8 percent return, each returning customer costs $25.

On these figures, the retailer should first fund the retention programs that keep current customers, then the win-back series, and spend on acquisition what remains, while seeking cheaper sources of new customers such as referrals. The point is not that acquisition is bad. It is that each dollar should go where it creates the most value, and the only way to know is to measure each program against a holdout group and compare the results in the same units.

There is also a moral side to the question. Customers who have trusted a business with repeated purchases deserve attention, not only the strangers the business hopes to win. A budget that pours money into attracting new customers while neglecting existing ones communicates where the business's heart is.

In practice, the retailer could start by splitting next month's budget into the three programs, holding out a random tenth of each target group, and comparing the margin each program produced per dollar after ninety days. The results would replace the habit with evidence, and the next month's budget could move toward whichever program earned the most.

Over a year, those monthly comparisons would also show whether the best mix changes with the seasons, since win-back may pay most in the months before customers usually reorder.

Reply to Grace

Grace, your plan to build a churn model and send retention offers to the 10 percent of customers most likely to leave sounds efficient, and you are right that prediction should guide retention spending. I would add two findings that might change whom the offers reach.

First, models differ in how well they predict. Neslin et al. (2006) organized a tournament in which researchers and practitioners submitted churn prediction models for the same data, and they found that predictive accuracy varied considerably across approaches, with more accurate models producing meaningful financial gains when used to target retention programs. Your company would benefit from testing more than one model on past data before choosing.

Second, and more surprising, accurate prediction of risk may not be enough. Ascarza (2018) ran field experiments with retention campaigns and found that targeting the customers with the highest predicted risk of leaving could be ineffective, because those customers were not necessarily the ones whose behavior the campaign changed. Some high-risk customers will leave whatever the company does, and some moderate-risk customers can be persuaded to stay. The better target is the customer whose likelihood of staying responds most to the intervention, which can be learned by testing offers on random groups across risk levels.

In Ezekiel, God condemns shepherds who failed the flock and promises to seek the lost, bring back the strayed, bind up the injured and strengthen the weak (English Standard Version Bible, 2001/2016, Ezekiel 34:16). The passage is about God's care for His people, but it pictures care that is matched to need: the strayed are sought, the injured are bandaged. A retention program can reflect that kind of attentiveness by asking what each customer needs, not only who is most likely to leave. A customer leaving because of a billing problem needs the problem fixed, not a discount. Would your company be able to run a small test of the offer across risk levels before rolling it out?

Reply to Nathan

Nathan, your case for a loyalty program at the regional coffee chain is persuasive, especially your point that regulars already feel attached to their local shops. Research supports building the program on that attachment rather than replacing it with points.

Bolton (1998) studied customers of a continuous service and found that cumulative satisfaction was positively related to how long customers stayed, and that customers with longer histories gave more weight to their accumulated satisfaction and less to a single new experience. For your coffee chain, this means the program should reinforce the experience that builds satisfaction, such as recognizing regulars by name and remembering their orders, rather than relying on discounts alone.

Bolton et al. (2000) found that members of a company's loyalty program tended to discount negative evaluations of the company compared with competitors when deciding whether to continue, and that membership was associated with greater retention. The program, in other words, can help the chain keep customers through occasional bad days, such as a long line or a wrong order, which every coffee shop has.

Proverbs observes that a friend loves at all times (English Standard Version Bible, 2001/2016, Proverbs 17:17). A loyalty program cannot create friendship, but it can honor the faithfulness customers already show and respond with faithfulness in return, through consistent quality, fair rewards and quick apologies when things go wrong. Programs that treat loyalty as a relationship rather than a transaction tend to last. Surprise rewards also fit the research on satisfaction, since an unexpected kindness adds to the store of goodwill that carries a customer through a bad day. They could be tracked in the same system as points, so the chain learns whether regulars who receive them visit more often over the following months. One question: would the chain let baristas give occasional surprise rewards to regulars, which might feel more personal than points?

References

Ascarza, E. (2018). Retention futility: Targeting high-risk customers might be ineffective. Journal of Marketing Research, 55(1), 80-98. https://doi.org/10.1509/jmr.16.0163

Bolton, R. N. (1998). A dynamic model of the duration of the customer's relationship with a continuous service provider: The role of satisfaction. Marketing Science, 17(1), 45-65. https://doi.org/10.1287/mksc.17.1.45

Bolton, R. N., Kannan, P. K., & Bramlett, M. D. (2000). Implications of loyalty program membership and service experiences for customer retention and value. Journal of the Academy of Marketing Science, 28(1), 95-108. https://doi.org/10.1177/0092070300281009

English Standard Version Bible. (2016). Crossway. (Original work published 2001)

Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C. H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 43(2), 204-211. https://doi.org/10.1509/jmkr.43.2.204

Roberts, M. L., & Zahay, D. (2018). Internet marketing: Integrating online and offline strategies (4th ed.). Cengage Learning.

Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109-127. https://doi.org/10.1509/jmkg.68.1.109.24030