Chapter 9: Engage: Building Loyalty and Co-Creating With Customers
Overview
This last chapter covers activities associated with the Engage stage: how to evaluate and encourage customer engagement and loyalty and foster co-creation by engaged customers. We discuss the importance of customer engagement, customer lifetime value, ways to measure engagement, consumption communities, and co-creation activities.
Learning Objectives |
Understand the concepts of engagement and loyalty, how to calculate customer lifetime value and its importance in marketing strategy, how to measure engagement, and how to create value with consumers. |
Engage
A widespread definition of engagement attributed to Forrester is “creating deep connections with customers that drive purchase decisions, interaction, and participation, over time.” Accordingly, the two objectives of the Engage stage are to (1) foster loyalty and (2) co-create value with customers.
Key performance indicators at this stage help measure a firm’s success in attaining these objectives and the achievement by consumers of associated goals. KPIs include the number of shares, brand mentions, referrals, repurchases, and reviews as well as the ratio of comments to posts, comments to likes, and reviews to sales.
The Engage stage is central for many reasons. Perhaps most importantly, recent research shows that loyalty leaders “grow revenues roughly 2.5 times as fast as their industry peers and deliver two to five times the shareholder returns over the next 10 years” (HBR). Working on increasing engagement is thus profitable. There are a few factors that explain this.
Acquiring customers is much more costly than retaining and selling to existing ones, and repeat consumers tend to spend more than new ones (Forbes). Engaged consumers are also more willing to interact with you, facilitating market research and leading to ground-breaking insights. This is particularly true since you can develop winning engagement strategies by identifying what makes your loyal customers loyal. Last, engaged customers work on your behalf, co-creating content that, as we’ve seen, is used by other consumers throughout their journey.
To better understand the value of customers over their lifetime with a company, we turn our attention to the concept of customer lifetime value. We then look at two tools that can help us better understand and measure customer loyalty. We conclude the chapter by examining value co-creation.
Customer Lifetime Value
Customer lifetime value (CLV) represents a customer’s profitability over their entire relationship with the business. A straightforward way of thinking about CLV is as follows:
CLV = average profit per sale (AP) × number of repeat transactions in a period (RTP) × retention time (RT)
Please note, however, that this is a simplistic approach used to illustrate this concept and not something we would recommend using in a real-life setting.
Let’s use the example of a subscription business (i.e., period = 1 month). The business has a churn rate of 2%. Churn rate represents the rate of customers leaving a company per period (Wikipedia). In this case, the company is losing 2% of its customer base every month. Churn rate is useful to calculate the average retention time of customers: By dividing 1 by the churn rate, we obtain the retention time. In this case, customers stay with the business for an average of 50 months (or 1 divided by 0.02). The average profit per sale is $30. The number of repeat transactions per period is one, because customers are making one transaction per month and the period we are looking at here is one month. The CLV is thus CLV = AP × RTP × RT. Since AP = $30, RTP = 50, and RT = 1, CLV = 30 × 50 × 1 = $1,500. Over their lifetime, each customer brings the business $1,500. |
CLV draws our attention to the importance of catering to the lifetime of a customer with a business. The first sale to a customer is not what typically brings revenue to a firm. Acquisition costs for a customer are generally much higher than the revenue a firm will make on its first sale. Thus, the objective of firms is to engage customers to increase their lifetime value.
More concretely, CLV can play many roles for a firm. For example, it helps firms price their customer acquisition strategies and calculate their return on investment. This is important because it helps evaluate whether acquisition strategies are profitable and manage marketing efforts more generally.
Continuing with the example above, let’s assume the firm is running a PPC search ad campaign to acquire customers. In this simple example, let’s further assume that people search for something, click on an ad which leads them to a landing page, and convert to customers from this landing page. The total campaign cost is $20,000, including all campaign elements (i.e., developing the landing page, all costs related to ads, etc.). The campaign gets 2,500 visitors on their landing page. The conversion rate is 5%, meaning that the firm converted 5% of the 2,500 visitors to their landing page. That works out to 125 customers (2500 × 5% = 125). The cost per acquisition is thus $160, or $20,000/125. |
At this stage, firms will be asking themselves, “Is this profitable? What is my return on investment? Should I continue running this acquisition strategy campaign?” CLV becomes useful at this stage.
As a reminder, this firm earns $1,500 per customer on average throughout their lifetime with the company. Even if the company only makes $30 on the first sale (meaning that they just “lost” $95, since it cost them $160 to acquire the customer), two rules of thumb help us see that this is a profitable customer acquisition strategy over time.
The two rules of thumb to quickly gauge whether a customer acquisition strategy is profitable are:
- Am I recovering my cost per acquisition over the next 12 months of the life of the customer with my business? In this case, the answer is yes: The company will make $360 per customer (AP × 12 = $30 × 12 = $360).
- Is my CLV more than three times my cost per acquisition (CAC) (that is, CLV/CAC > 3)? In this case, the answer is also yes. CAC is $160 while CLV is $1,500, and CLV/CAC = 9.375. In fact, the firm should be happy to pay up to $500 per acquisition.
Among many other uses that CLV serves, it can also support retention and customer support strategies central to the Engage stage. By knowing the lifetime value of customers, firms can more easily price retention and support strategies, i.e., how much to put into trying to retain customers.
CLV varies per persona, where some personas will be worth more over their lifetimes than others. This helps firms to decide where to spend extra resources and which personas to pamper a bit more. It can also help a firm see whether it should “fire” a persona, i.e., minimize the efforts dedicated to customers already acquired and stop acquisition strategies for a specific persona if their CLV is drastically lower than that of other personas.
Lastly, it is important to keep in mind that, apart from subscription businesses such as the example above, customers rarely bring in the same amount to a firm throughout their lifetime. The relationship between a customer and a firm evolves over time, and it is important to recognize that the journey of customers expands beyond their first purchase with a firm. Not only does this vary between personas, but it might also vary between markets. In some markets, such as videogame consoles or eyewear, products are seldom sold, with an extended period between purchases that might encourage churn. In other markets, like groceries, consumers are continuously making purchases over their lifetime. As is the case in the market for diapers, other markets might see a significant uptick at the start of the customer’s life with a company and then declining sales over time as, in the case of diapers, the baby ages into a child. Although the new approach is predictive analysis, some earlier analytical tools, such as RFM analysis (discussed in the next section), provide information regarding some of these aspects. They also help us understand the basics of analyzing customer behavior to make strategic decisions.
RFM Analysis
RFM, which stands for recency, frequency, and monetary value, is a long-standing analytical method that helps analyze and segment customer behavior based on the recency of their last purchase, the frequency of their purchases, and their monetary value, i.e., how much they spend with the firm.
By helping firms understand the purchasing behavior of acquired customers, RFM analysis can help increase retention and purchase per customer, identify which customers are not so great, better, and best, whether we are experiencing issues with a specific persona in terms of repurchase behavior, and so on.
To conduct an RFM analysis, a firm starts with its customer database. The first step is to assign value to customers associated with their recency, frequency, and monetary value. Since RFM analyses can be done by operationalizing these variables differently, let’s assume here that recency refers to the recency of the last purchase in days, frequency to the frequency of purchases over three months (or a quarter), and monetary value to the total amount spent during this period.
Firms will often start by indicating the exact number associated with each variable and move to create categories for each. For example:
Recency | Frequency | Monetary value | |
1 | Very recent | Very frequent | High value |
2 | Recent | Frequent | Medium value |
3 | Not recent | Infrequent | Low value |
They will perform their analysis with these categories.
We can then create segments by combining these together. The following table shows examples of such segments, where “x” stands for any number (i.e., its value is not important for defining that segment).
Segment | Recency | Frequency | Monetary value |
Best customers | 1 | 1 | 1 |
Loyal customers | x | 1 | x |
Big spenders | x | x | 1 |
Lost or almost lost customers | 3 | 1 | 1 |
Thrifters | 3 | 3 | 1 |
Then, each customer will be coded based on the categories created above, as shown in the following table.
Customer | Recency | Frequency | Monetary value |
Jack | 3 | 3 | 1 |
Jill | 1 | 1 | 1 |
Bill | 3 | 1 | 2 |
Sean | 3 | 1 | 3 |
Raymond | 2 | 2 | 2 |
Tom | 1 | 1 | 1 |
Tina | 3 | 3 | 1 |
Mariah | 2 | 3 | 2 |
Sanjit | 1 | 3 | 3 |
Todd | 1 | 2 | 3 |
Becky | 1 | 1 | 1 |
Seth | 2 | 3 | 2 |
Caroline | 3 | 2 | 1 |
This allows firms to categorize customers into the categories just created (e.g., best customers, loyal customers, etc.). These categories of customers can help decide which segments to concentrate on and what kind of strategy to use to engage customers. Examples could include performing retention campaigns with big spenders, recuperating almost lost customers, or moving loyal customers to increase their monetary value over time. A firm could also target its best customer segment: send an appreciation letter, analyze their personal preferences for more personalized offers, or generally develop strategies to keep this segment highly satisfied.
Although simple, RFM analysis is a useful tool to foster engagement. A more thorough analysis could combine RFM with personas and evaluate whether personas also share commonalities or differences in their purchasing behaviors, leading to the creation of even more personalized campaigns.
Net Promoter Score
Another approach to measuring customer satisfaction and engagement that is widely used is the net promoter score (NPS). Described by the Harvard Business Review as “the one number you need to grow,” NPS is associated with a single, one question survey based on customer engagement that has shown over time to be a great predictor of firm success.
To calculate the NPS, a firm first asks the following question to its customers: “How likely is it that you would recommend our company/product/service to a friend or colleague?” (Note: NPS has also been used in the past with other types of respondents, such as employees or resellers, depending on which population a company wants to measure). Respondents are asked to answer using a 0 to 10 scale, with 10 being “extremely likely” and 0 being “not at all likely.” The NPS is then calculated by subtracting the percentage of detractors from the percentage of promoters. The result ranges from −100% (all detractors) to 100% (all promoters) (Figure 9.1).