Nurture Customers

Based on the works of cognitive psychologists, marketers started to develop and adopt decision-making processes leading to a so-called conversion funnel. With every stage achieved, the conversion of consumers to customers becomes more likely. Sharing links to hierarchy-of-effects models such as AIDA (Kotler and Armstrong 2011), the most popular conversion funnel divides the customer journey into an awareness (upper funnel), a consideration (middle funnel) and a purchase stage (lower funnel) (Jansen and Schuster 2011).

Because each touchpoint along the customer journey affects conversion, companies need to be aware of the different stages within the decision-making process. Within the awareness phase, the consumer recognizes his need and explores options that could satisfy this need. In this stage, consumers expect objective information about the product/service to build a fundament for decision-making. In the consideration phase, the consumer evaluates and compares the different options. Responsive to more detailed information, he might turn to comparison sites. The final decision is made during the purchase phase when the consumer converts to a customer.

Because of the various customer journeys consumers encounter, decision-making processes may be clustered into low- and high-consideration decisions. Low-consideration decisions, e.g., buying Fast Moving Consumer Goods (FMCG), typically involve a short transaction cycle. Decisions are made easily and quickly without intense information retrieval. Marketers focus on creating awareness through social content and by including influencers. Products or services acquired in this low-consideration set are typically referred to as low-involvement goods. In contrast, high-involvement goods typically require a high-consideration decision, e.g., buying a car, with multiple decision-makers, extensive information research through a multitude of channels with many decision criteria involved. Hence, high-involvement goods require an effortful and long-term decision-making process. Statistics show that 69% of German consumers start researching three months before their potential car purchase. These consumers spend around ten hours in researching before visiting a retailer or dealer. Further, 60% of the respondents said they seek advice online. (Deloitte 2018). Overall, the extensive consideration phase provides opportunities for companies but also bears the risk of lowered influence once the consumer has reached the purchase phase and initiates contact with a company.

Promotional mix in marketing along the conversion funnel

With customer journeys evolving, companies need to adapt by changing their communication strategy and channels accordingly. Companies target potential customers through a mix of offline and online communication channels, the media mix. Previously, companies focused on outbound marketing known as pushing information. Now, they emphasize the importance of attracting consumers through inbound marketing known as pulling consumers. This approach uses content marketing and search engine marketing (SEM) to offer relevant content and attract attention. Even though, consumers still consult both consumer-initiated communication (CIC) and firm-initiated communication (FIC) through which companies initiate marketing interventions. A consumer evaluates each available channel according to its ability to guide him/her to the desired information in the most cost- and time-effective manner.

Advertising effectiveness

Measurement

Achieving (or optimizing) advertising effectiveness requires an appropriate outcome variable, termed indicators of advertising effectiveness. These indicators often include return on investment (ROI), return on marketing investment (ROMI), cost per win, cost per lead, conversion rate, incremental sales or customer lifetime value. A channel with a higher indicator (or for cost: lower) should be given more budget, that is, more budget should be allocated to this channel. A growing body of literature has examined the relationship between investment in advertising and profit.

Online channels contribute to the achievement of increased measurement quality as compared to offline channels.

Effects

We incorporate a wide variety of online advertising channels (i.e., referral, paid email, paid search, display ads) and assesses their impact on conversion according to funnel stages. Even though the psychological component impacting consumer decision-making cannot be revealed, our research considers the influence of time, particularly carry-over effects (preceding advertising expenses). Finally, the methodology behind our study is needed to generalize the findings in different contexts.

RFM(E) analysis overview

Digital marketers start to understand the importance of “know your customer.” Instead of focusing on generating more clicks, marketers need to shift their focus to retention, loyalty and building customer relationships.

Instead of analyzing the entire customer base as a whole, it’s better to:

• segment your customers into homogeneous groups,
• understand the traits of each group,
• and engage them with relevant campaigns rather than segmenting based on demographic criteria like country, sex or age.

One of the most popular, easy-to-use, and effective segmentation methods which enables marketers to analyze customer behavior is the RFM (Recency, Frequency, Monetary) or RFE (Recency, Frequency, Engagement) analysis. RFM segmentation categorizes your customers into different segments, according to their interactions with your website, which will allow you to subsequently approach these groups in the most effective way. 

What is RFM Analysis?

RFM is a behavior-based segmentation approach, which stands for Recency, Frequency, and Monetary value, each corresponding to key customer trait. These RFM metrics are important indicators of a customer’s behavior because:

• frequency and monetary value affect a customer’s lifetime value, and
• recency affects retention, a measure of engagement.

Businesses that lack the monetary aspect, like viewership, readership, or surfing-oriented products, could use Engagement parameters instead of Monetary factors. This results in using RFE – a variation of RFM. Furthermore, this Engagement parameter could be defined as a composite value based on metrics such as bounce rate, visit duration, number of pages visited, time spent per page, etc.

RFM factors illustrate these facts:

• The more recent the purchase, the more responsive the customer is to promotions
• The more frequently the customer buys, the more engaged and satisfied they are
• Monetary (Engagement) value differentiates heavy spenders from low-value purchasers or users

It helps managers to run an effective promotional campaign for personalized service and can helps marketers find answers to the following questions:

• Where to focus your efforts in order to receive bigger value?
• How to segment your customers based on their behavior and patterns?
• How to build personalized marketing retention campaigns?
• Who are your best users?
• Which of your customers could contribute to your churn rate?
• Who are your potential customers?
• Which of your customers can be retained?
• Which of your customers are most likely to respond to engagement campaigns?

RFM analysis details

Let’s demonstrate how RFM works by considering a sample dataset of customer transactions. Table 1 contains recency, frequency, and monetary values for 15 customers based on their transactions.

Table 1: Example Customer transactions dataset

RFM Analysis Example

There are many ways in which the RFM score can be calculated. Start with using the discretization method and then calculate the score using the arbitrary/expert split.

1. Discretization

With discretization, customers are automatically divided into quartiles/quintiles (segments) of relatively equal size. The resulting four/five groups will all have almost identical numbers of customers.

2. Arbitrary/expert split

With the arbitrary split, each marketer defines a fixed set of intervals where certain values equal certain scores. For example, R score 1 is granted to the customer who bought a product last time six months ago, R score 2 is granted to the customer who bought it three months ago. The challenge using arbitrary/expert split is that you need to choose the right values for the intervals and sometimes there are errors or wrong assumptions made.

We advise using values in the report on the discretization level. Assume that we rank these customers from 1-5 using RFM values.

Let’s begin with ranking customers on recency first, as shown in the below table:

As seen in the above table, we have sorted customers by recency, with the most recent purchasers at the top. Since customers are assigned scores from 1-5, the top 20% of customers (customer 12, 11, 1) receive a recency score of 5, the next 20% (the next 3 customers 15, 2, 7) a score of 4, and so on.

Similarly, we can then sort customers by frequency from most to least frequent, assigning the top 20% a frequency score of 5, etc. For the monetary factor, the top 20% of customers (big spenders) will be assigned a score of 5 and the lowest 20% a score of 1. These F and M scores are summarized below:

RFM Score

Finally, we can rank these customers by combining their individual R, F, and M rankings to arrive at an aggregated RFM score. This RFM score, displayed in the table below, is simply the average of the individual R, F, and M scores, obtained by giving equal weights to each RFM attribute.

Recency, Frequency, and Monetary Analysis

The next question that arises is: Is it fair to average out the individual R, F, and M scores for each customer and assign them to RFM segment, as per their purchase or engagement behavior?

Depending on the nature of your businesses, you might increase or decrease the relative importance of each RFM variable to arrive at the final score. For example:

• In a consumer durables business, the monetary value per transaction is normally high but frequency and recency is low. For example, you can’t expect a customer to purchase a refrigerator or air conditioner on a monthly basis. In this case, a marketer could give more weight to monetary and recency aspects rather than the frequency aspect.
• In a retail business selling fashion/cosmetics, a customer who searches and purchases products every month will have a higher recency and frequency score than monetary score. Accordingly, the RFM score could be calculated by giving more weight to R and F scores than M.
• For content apps like Hotstar or Netflix, a binge-watcher will have a longer session length than a mainstream consumer watching at regular intervals. For bingers, engagement and frequency could be given more importance than recency, and for mainstreamers, recency and frequency can be given higher weights than engagement to arrive at the RFE score.

This simple approach of scaling customers from 1-5 will result in, at the most, 125 different RFM scores (5x5x5), ranging from 111(lowest) to 555(highest). Each RFM cell will differ in size and vary from one another, in terms of the customer’s key habits, captured in the RFM score. Obviously, marketers can’t analyze all 125 segments individually if each RFM cell is considered a segment, and it’s difficult and overwhelming to visualize this imaginary 3D cube!

In general, the monetary aspect of RFM is viewed as an aggregation metric for summarizing transactions or aggregate visit length. Therefore, these 125 RFM segments can be reduced to 25 segments by using just R and F scores.

Create score-based segments

Categorizing the users and put them into segments is the last step of our effort. There are various approaches and we advise each business to review 125 segments and define its 11 categories, normally we conduct a workshop session in order to guide the process. The table below is something which we recommend to be used as a starting point and is not perfectly suited for all projects as your customer composition might drastically differ. Therefore, use the table as a starting point and if needed make the necessary adjustments.

RFM segments

Based on the calculated RFM score, the customers are put into 11 segments:

Advantages and limitations of RFM-analysis

The chosen example shows that RFM-analysis can be used for a wide range of business units and business cases. The assumptions and attributes of the model can be changed in a targeted manner. For example, customers can be split up in advance based on certain characteristics (e.g., by industry) and the RFM-analysis can thus be performed on an industry-specific basis.

RFM-analysis can also be extended by the Length parameter (LRFM-analysis), which describes the number of days since the first contract was signed and how long the customer has been stored in the database.

Furthermore, the computed parameters are well suited as features for clustering methods from the Machine Learning area and thus offer a foundation for forecasting models. However, this shows the limitations of RFM-analysis, as it is not able to predict the future behavior of a customer on its own. It can only access past data and, for example, make comparisons with the customer’s behavior in previous years. Nevertheless, the analysis is suitable for deriving short-term actions (personal contact, discounts, etc.)

ShopUp CDP AI platform helps online businesses benefit from RFM(E) Analysis

The ShopUp CDP AI platform offers RFM analysis tailored to various businesses. Our clients execute full RFM analysis based on user-defined criteria, risk analysis, and schedule optimization. Learn more about ShopUp CDP. 

Building a predictive churn model helps you make proactive changes to your retention efforts

It is no secret that customer retention is a top priority for many companies; acquiring new customers can be several times more expensive than retaining existing ones. Furthermore, gaining an understanding of the reasons customers churn and estimating the risk associated with individual customers are both powerful components of designing a data-driven retention strategy. A churn model can be the tool that brings these elements together and provides insights and outputs that drive decision making across an organization.

What is Churn?

In its simplest form, churn rate is calculated by dividing the number of customer cancellations within a time period by the number of active customers at the start of that period. Very valuable insights can be gathered from this simple analysis — for example, the overall churn rate can provide a benchmark against which to measure the impact of a model. And knowing how churn rate varies by time of the week or month, product line, or customer cohort can help inform simple customer segments for targeting as well.

However, churn is often needed at more granular customer level. Customers vary in their behaviors and preferences, which in turn influence their satisfaction or desire to cancel service. Therefore, a cohort-based churn rate may not be enough for precise targeting or real-time risk prediction. This is where churn modeling is usually most useful.

The output of a predictive churn model is a measure of the immediate or future risk of customer cancellation. This is what the term “churn modeling” most often refers to, and is the definition we will adhere to in this post.

Note that the rows in the above matrix are not mutually exclusive: Involuntary churn can be present in either contractual or non-contractual settings.

Churn is especially relevant in contractual circumstances, which are often referred to as a “subscription setting,” as cancellations are explicitly observed. However, non-contractual businesses also benefit from modeling churn. The challenge, in those case, lies in defining a clear churn event timestamp. This is often done by finding a certain threshold for a period of inactivity and using it as a definition for the churn event.

On the other hand, voluntary and involuntary churn might be caused by different underlying factors. Voluntary churn is often more prevalent than accidental churn due to events such as payment failures. It is also more difficult to determine the root cause of voluntary customer cancellations, which is why most churn literature focuses on voluntary churn events. While both voluntary and non-voluntary cancellations have a clear revenue impact, it is best to focus a churn model on only one type of churn.

We can classify customer churn by grouping cases into different categories:

Contractual Churn: This type of churn is applicable to businesses that provide different services such as cable companies. It happens when customers decide not to continue with their expired contracts.

Voluntary Churn: It refers to the situation in which customer decides to cancel their existing service and can be applicable for companies that provide a service not based in a fixed-term contract, such as prepaid cellphones or streaming subscriptions.

Non-contractual Churn: This type of churn, which is applicable to businesses that depend on retail locations or online stores as an example, can be associated with consumers leaving a possible purchase without completing the transaction.

Involuntary Churn: This happens when a customer can no longer stay with the credit card company or can not pay their credit card bill.

Use Cases

The probability of churn can be predicted using various statistical or machine learning techniques. These methods process historical purchase and behavior data in order to predict the probability of cancellation per customer.

A well-constructed model can inform a wide range of decisions and flow into numerous internal tools or applications. For example, some common use cases for a churn model are:

• Measuring feature impacts on the likelihood of churn in order to understand why customers choose to leave, which can inform long-term retention initiatives
• Creating churn risk scores that can indicate who is likely to leave, and using that information to drive retention campaigns
• Predicting the probability of churn and using it to flag customers for upcoming email campaigns
• Integrating outputs with internal apps, such as a customer call center, to provide relevant real-time churn risk information
• Discounting strategically with promotion campaigns to customers with a high cancellation risk
• And many more …

Marketers know it costs less to retain a customer than to acquire a new one. But many struggle to keep existing customers engaged due to budget or resource constraints.

What are customer retention models and what value do they provide?

Summary

Lifetime value is projected revenue per customer during her/his lifetime for your company (website).

It’s been called the most important metric for eCommerce and SaaS companies, and with good reason. It’s also one of the more underappreciated, perhaps because many brands struggle to properly define and calculate it. With this article, we are going to reveal this mystery by giving some definitions, examples, data sources, insights, how to calculate this metric, and more useful information about CLTV that business owners and managers should know. 

Business overview

By measuring CLTV in relation to customer acquisition costs (CAC), companies can measure how long it takes to recoup the investment required to earn a new customer — such as the cost of sales and marketing.

At a glance, CLTV tells you how much a customer is worth to your brand and gives you insight into their overall value. Based on that information a better understanding and estimation of how much should be invested in customer acquisition and retention can make the process more transparent and clear for sales and marketing teams.

The sooner you start to track CLTV, the better information you will have about which of your marketing campaigns drive customer loyalty and increase overall revenue. 

Technical overview

Step 1 – Grab your sales data

It depends on your business, but usually, when we need to calculate it for an e-commerce business we need some main data from your deals or purchases with the following characteristics:

• Customer ID
• Orders date
• Order revenue

Step 2 – Calculate derived characteristics on customer level

There is a need to split active and not active users and then from the active segment to compute the following characteristics:

• “Months since first-order” – this represents the number of months since a customer made his first purchase up to now
• Average “Number of orders per month” per customer – This characteristic divides the total number of orders for a particular customer per number of months since the first purchase
• Average “Amount per Order” per customer. This is the average order amount for a particular customer

Step 3 – Calculate how many months a single customer stays with the company

There are different approaches to compute it, for instance, it can be an average of months since the first order for all active customers.

Step 4 – Calculate average monthly revenue per customer (LTV Score)

The last step is to derive several characteristics:

• Average Revenue per month(LTV Score) = Number of orders per month X Order Amount per month
• Then we split “LTV Score” to 5 or 10 equal BINS it is called TILE technique
• Now we know “LTV Level” (BIN) for each customer and its importance based on the bins it stays.

Step 5 – Calculate overall LTV or LTV for each segment

Once you compute LTV score you can compute:

• Overall LTV if you average LTV score for all customers
• LTV for each score level if you average them based on the level they are

Example

In this example, we provide data from an online store and it covers all the calculations we spoke about. 

Step 3 and 5 can be computed separately.

 Table1

In Table 1 you could see every customer has a score level and it is from 1 to 5. Such an approach could help you precisely calculate the expected revenue for every customer you gain at a specific price. 

Advantages and disadvantages

Advantages:

• Work on retention by increasing the number of purchases

If you’re looking at customer lifetime value as a way to justify spending more on the acquisition, you’re probably focusing on the wrong side of the equation. In actuality, the best way is to invest in retention by increasing the purchase frequency of your heavy spenders by recommending new products relevant to them using various channels. 

For instance: For every 1% of shoppers who return to your eCommerce site after their first visit, your revenue increases by approximately 10%. So if you retain 10% more of your existing customers, your revenue effectively doubles.

• Reduce your churn rate

A very common technique to reduce churn rate is by identifying and focusing on customers with minimal or moderate customer lifetime value and turning them into loyal customers. 

For example, if there is a decrease by 3-5 % of your churn rate your profitability can increase by anywhere from 25 to 95%.

• Incentivize repeat customers and nurture one-time customers

In eCommerce, the probability of selling to an existing customer is around 60-70%. Yet the probability of selling to a new shopper ranges from 5-20%. In addition, returning customers spend an average of 67% more than first-time customers. Knowing to focus on repeat customers—as well as getting one-time customers to make another purchase—clears the way for centering your marketing strategy on these segments in order to drive CLTV.

• Customer Loyalty

We all know about the Pareto Principle, which hypothesizes that 80% of your revenue comes from 20% of your customers. Not surprisingly, focusing on your most loyal customers makes a lot of sense for increasing your customer lifetime value.

Their overall spend. And then you calculate your brand’s CLTV, it’s easier to see and segment your highest-value customers, giving you a chance to target them with special campaigns intended to increase their loyalty—and why wouldn’t you, when just a 5% increase in customer loyalty can increase your average profit per customer by 25-95% ? 

Challenges:

• It Can Be Hard to Measure

If you don’t have quality tracking systems in place, calculating CLV can be difficult. An enterprise resource planning (ERP) or customer relationship management (CRM) system can make this information easily available on an automated dashboard that tracks KPIs.

• High-Level Results May Be Misleading

Looking at a business’s total CLV can be a helpful data point, but it can also cover up problems in certain customer segments. Breaking down the data by customer size, location and other segments may provide more useful data.

• Complex calculation

Differences in products, costs, purchase frequencies, and purchase volumes can make customer lifetime value calculations complex. However, with the right tools, you can find customer lifetime value in just a few clicks.

Conclusions

In this series of articles, we explain some of the most applicable models for every online business, with business and technical details, which can be used by everyone. Meanwhile, if you want to automate your business and access our expertise in solving your problems, please contact us. Also check Catwing (AI platform for eCommerce and SaaS) a tailored solution for each customer with fast deployment, numerous models, and AI automation. 

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