The busiest time of year is approaching and you’re running ads to increase acquisitions and revenue…
Let’s say you acquire two new customers at an acquisition cost of $20 each. Luke spends $50 & Jason just $25. Luke, therefore, represents a better ROI, right? Well, initially yes, but perhaps we’re thinking too short term as Jason returns at a later date and spends another $100.
Instead of focussing on short-term ROI, businesses should consider a customer’s lifetime value. Businesses need both Lukes & Jasons but by focusing on acquiring high-value customers revenues will increase, your business will grow and all shall be donning ear to ear grins.
If lifetime value does not exceed cost of acquisition, however… you get the picture.
So how do businesses identify, target and acquire high value customers while inspiring long-term loyalty? Well, we provide a tailored experience using RFM segmentation: Frequency, Recency & Monetary.
Imagine you’re the proprietor of a coffee shop (the kind that sells drinks prefixed with too many adjectives) & want to introduce a 50% discount voucher. Ok, great. Offering a discount to customers with a low frequency may be a great way of enticing them through the door.
Now consider a customer that religiously purchases their dirty, skinny chai latte each day without fail. Their frequency is already high so a 50% discount is probably the wrong approach. Instead, you could give them a free hamper on their birthday; you already know what they like.
In DV STREAM: LIfetime ROI, Suraj Pabari discusses how you can develop a strategy to retain high-value customers and increase loyalty with low-value customers.
by Suraj Pabari, Partner in Customer Analytics at SingleView, a data consultancy in Australia.
We can use RFM scores to define segments within our customer base.
These segments can be used to define relevant actions that we can take with customers to increase their overall lifetime value.
Key segments include ‘Superstar’ customers with high RFM scores, for which you could develop a premium service; ‘Promising’ customers with mid-range frequency and monetary value, for which you can run loyalty schemes to increase spend and frequency; ‘At Risk’ customers with low recency, for which you can send targeted messaging with a possible discount to bring them back.
Run experiments to test these different ideas, and start by focusing on the largest audiences that will drive the greatest impact, with clear success metrics and the next steps to scale the action.
How can I take action using lifetime value scores?
As you can see from the previous post on RFM segmentation, it is quite easy to get excited with your new RFM scores and to start generating elaborate experiments. However, the goal of any new project should initially be to keep any action simple and easy to implement.
The main action from the RFM scoring is to create a few key segments. You want the segments to be large enough in scale so that you can actually see a difference when you run experiments using the segments. You also want to make sure that the segments are updated on a regular basis, and easy to export, so that you can easily integrate them into your marketing platforms.
What segments could you create? I often focus on the following three segments, as I have found that running experiments with these segments has driven significant revenue impact. Feel free to play around with the RFM ranges for these segments based on your goals.
Superstars: High value customers with RFM score 555 (or if you want to sum the individual RFM scores then you can segment those customers with a total score between 10–15). These customers are your ‘superstars’ and should be treated as such. This is the concierge, the airport lounge, the free delivery, the ‘free bottle of champagne on the birthday’ type of customer. These customers are already loyal so whilst they may not need a ‘buy one get one free’ discount, they do need to know that they are special. As an example, with one of our beauty client, we were discussing how they could send gift baskets to these top customers over Xmas. How might you treat your best customers if you want them to stay? What type of service might you provide?
Promising: Customers with high R, and mid range F and M (4–5)(2–3)(2–3). This is where building loyalty is important. Send these customers a voucher, an incentive to get them to spend more and spend more frequently. Start a loyalty scheme. In Australia, we love our coffee, and cafes tend to have a lot of customers in this ‘Promising’ segment, with many customers who come back occasionally. These cafes sometimes use loyalty (‘buy 10 get 1 free’ schemes) to develop these Promising customers into Superstars. For this segment, you want to push your brand to the top of their list of options so that it remains top of mind for your customers and they have an incentive to keep coming back.
At Risk: Customers that are likely to churn, with RFM score ranges of (1–2)(4–5)(4–5). Initially focus on the customers with the low recency score, and high frequency and monetary value scores, the ones that you most want to save. Why do you think these customers aren’t coming back? How can you test this hypothesis? Since this customer group may not be too big, try personalised messaging or calls to remind these customers that they are important. Or more simply, send them an e-mail to convince them to come back, potentially using a special offer to bring your brand to their top of their consideration set.
Whenever you are implementing any of these changes, do so with a controlled experiment. As an example, if you wanted to test a premium service offering for your high value customers, randomly segment them into two groups. Give one group the premium service, and do not give the other group the same offering. Set distinct control and test groups, and keep everything else the same. Then observe how spending varies over a fixed period of time.
Never just implement a change without an experiment. In the above example, if you were to run this premium service with all customers, and you saw frequency of purchase improve, how would you know that the positive change was a result of the service offering and not the result of some other factor, such as good weather?
But what about using this data to acquire new customers?
A lot can be said about finding ‘lookalike audiences’ to your high value audiences and importing them into your marketing platform to prioritise. These need to be tested just like anything else, with clear experiments with clear success metrics (are you looking for a greater number of clicks, a higher conversion rate, or increased spend?) I often like to think about the time vs reward of developing these experiments; it’s important that the audience size is large enough to drive significant change in core metrics to be worth the increased time and complexity in management of these lists.
I hope you now have a better idea about the different ways you can use the RFM scores to develop segments, and develop experiments to drive increases in overall lifetime value.
In the next post we will be discussing the “whys” and the “hows” of predictive lifetime value modelling (versus historical lifetime value modelling with RFM). This model uses customer behaviour to predict how many times a customer will visit, what they will spend and the likelihood that they will churn to give us an estimate of lifetime value. You can also use this data to understand which customer attributes (age, gender etc.) are correlated to your high value customers.
How do you currently segment your customers? What actions do you take for the different segments? Add your comments below.
by Suraj Pabari, Partner in Customer Analytics at SingleView, a data consultancy in Australia.
Currently, many businesses value customers based on their first purchase. As a result, they may under-invest in customers that would be higher value over a period of time and over-invest in lower value customers.
However, we can value customers based on their lifetime value, which is the total spend of the customer over a given period of time.
One way to measure lifetime value is to segment based on recency, frequency and monetary value.
By calculating RFM scores for our customers, we can segment our customers and understand the highest value customers,as well as those with low frequency, low average spend and those who have not visited for a long while.
We can then take action based on these segments to increase long term business revenue.
What do we mean by lifetime value?
You acquire a new customer. Not only that, but the customer returns again and again, spending hundreds of dollars every time they return. This customer must really love you! However, traditionally, you value this customer based on the revenue from their first purchase and now, this customer does not look so good. In fact, you would pay the same for this superstar customer as you would pay for his discount-seeking neighbour, who only bought something because it had a heavy discount. But do not fret, as we are moving to a new world, a world in which the customer is valued based on their total spend over a defined time period… or their lifetime value. Now, you can more easily find the valuable superstars and give them that star treatment with the hope that they stay with your business, and help you discover more just like them, transforming your business into a business of superstars.
The problem with big shiny things
Let’s imagine an extremely technical person in your business spends six months on building a model to predict the customers that will churn in the immediate future. They tell you that they have found a number of relevant features that predict churn, such as delay in bill payment, location and even age. They even excitedly proclaim that they have refined the model to the point where the model can find almost 90% of the customers that will churn. You are excited! You decide to run an experiment by showing ads to customers that are likely to churn to try to prevent them from leaving. However, clicks are so low, that you see no difference in churn rate. You go home, dejected.
Can you relate? After nine years in the Marketing Analytics space, I certainly can. And I’m not the only one.
We often hear buzzwords about things we ‘should do’. ‘Optimise to lifetime value,’ they say. ‘The customer journey is complex, implement a custom attribution model,’ is a common recommendation. ‘Most of your sales are occurring offline, why not connect offline data?’ is often proposed as a solution. You spend time and money on these pet projects, thinking they will transform your business and you divert resources away from your acquisition marketing efforts. The results are often far from impressive.
Should you avoid these types of projects? Absolutely not. The intentions of these projects are generally sound. However, my learning has been that these projects only work when you think about ways that you can use these programs to drive value before you invest significant resources. Write these specific actions down. Get endorsement from other teams that need to be involved. Quantify the value (with some justifiable logic!)
One particular project that often can drive a high cost despite demonstrating a low return is ‘lifetime value’. However, hopefully by the end of this article you will realise that lifetime value can drive significant business growth, in ways that are neither time consuming nor expensive.
The most valuable customers are the ones who have spent the most, right?
To start with, let’s discuss why lifetime value is so important. The current state of measurement with many businesses can be demonstrated in the graphs below. Assume the bars represent the value of five individual customers and the horizontal line represents the cost to acquire a customer: naturally, you set that cost to be equal to the value that you will get from the customers to break even (or even lower if you want a higher margin). You can see that by optimising to this cost you end up over-investing in some ‘low value’ customers (shaded red) and under-investing in other ‘high value’ customers (shaded green).
In contrast, if you were to focus on acquiring more high value customers (the green bars), in many cases, the long term revenue would be higher, as can be seen below.
So what do we mean by high value customers? The ones that spend the most money?
Not quite. Often using total spend over a period of time can be a good proxy, but it is important to take a more nuanced view. Take the example below. Which customer do you think has the greatest value to the business?
Were you able to guess correctly? The behaviour of the first customer suggests that they will be more loyal, as they have greater consistency and bought more recently. We saw this with one of our hospitality clients: they had customers spending a lot of money in their venues on expensive champagne, and then not spending anything for a long period; we had to think of them differently to those customers who spent the same amount, but over a more extended period. The former may have been enjoying themselves on a luxury holiday, and since they will not be coming back, it may not make sense to define them as a high value customer.
So how do we define a high value customer then?
We have a fairly simple way of measuring high value, which is based on three factors:
Recency = How recently the customer bought. Someone who bought a year ago is at risk of churning and may not be high value versus someone who bought last week. Note that, in some models, recency refers to the gap between the first and last purchases.
Frequency = Number of repeat purchases. More purchases demonstrates greater loyalty. Using frequency in this way allows us to distinguish between the tourist who came to the venue and spent a lot of money whilst she was on holiday, and the businessman who goes to the same venue every week.
Monetary value = Average order value. We sometimes exclude the first purchase in this average, particularly if the purchase has been driven by a voucher.
This RFM method gives us a score for each factor. The simplest way to come up with a score is to rank the value that each customer has for each variable between 1 and 5 (though the exact scoring might differ by business). As an example, for recency: bought within last week = 5, bought within last month = 4, bought within last year = 3 etc.; for frequency: 10+ purchases = 5; 8–10 purchases = 4 etc. The recommended approach to determine the actual boundaries is to ensure that 20% of the customers are in each bucket.
Now the fun begins!
With some simple maths (which can even be done in a spreadsheet) you now have three scores per customer. What does this mean?
Let’s say a customer has a RFM score of 555. Keep this customer close! They have recently bought a high value item, and will often return to buy high value items. But what about a 155? With a low recency, this customer hasn’t bought anything recently. Why haven’t they bought anything? How can we bring them back? This diagram makes it simpler to understand.
..though note that in reality we are looking at a cube as a pose to a square!
You may already be thinking about some of the things you can do with this data. What could you do with a ‘High Spender’ to increase their frequency? Would you give the ‘Superstar’ a voucher or would you instead offer them a ‘concierge’ service? Now, rather than simply saying: “These are my high value customers and these are my low value customers,” you can answer some more complex questions, such as:
Who are my highest value customers?
Which customers are on the verge of churning?
Which customers have the potential to be transformed into higher value customers? How might we do that: by trying to upsell them, or getting them to return more often?
Who are the low value customers that you can ignore?
Which group of customers is most likely to respond to your current campaign?
I hope you now have a better idea about the power of LTV segmentation, and understand how you can segment your customers using RFM methodology to answer some important questions.
How do you currently segment your customers? How do you leverage the insights from your segmentation? Feel free to add some comments in the post!