customer intelligence

1月 162020
 

Using Customer Lifetime Value in your business decision making is often important and crucial for success. Businesses that are customer-centric often spend thousands of dollars acquiring new customers, “on-boarding” new customers, and retaining those customers. If your business margins are thin, then it can often be months or quarters before you start to turn a profit on a particular customer. Additionally, some business models will segment the worth of their customers into categories that will often give different levels of service to the more “higher worth” customers. The metric most often used for that is called Customer Lifetime Value (CLV). CLV is simply a balance sheet look at the total cost spent versus the total revenue earned over a customer’s projected tenure or “life.”

In this blog, we will focus on how a business analyst can build a functional analytical dashboard for a fictional company that is seeing its revenue, margins, and a customer’s lifetime value decrease and what steps they can take to correct that.

We will cover 3 main areas of interest:

  1. First, screenshots of SAS Visual Analytic reports, using Customer Lifetime Value and how you can replicate them.
  2. Next, we will look at the modeling that we did in the report, with explanations on how we got used the results in subsequent modeling.
  3. Lastly, we talk about one example of how we scored and deployed the model, and how you can do the same.

Throughout this blog, I will also highlight areas where SAS augments our software with artificial intelligence to improve your experience.

1. State of the company

First, we will look at the state of the company using the dashboard and take note of any problems.

Our dashboard shows the revenue of our company over the last two years as well as a forecast for the next 6 months. We see that revenue has been on the decline in recent years and churns have been erratically climbing higher.

Our total annual revenue was 112M last year with just over 5,000 customers churning.

So far this year, our revenue is tracking low and sits at only 88M, but the bad news is that we have already tripled last year's churn total.

If these trends continue, we stand to lose a third of our revenue!

2. The problems

Now, let’s investigate as to where the problems are and what can be done about them.

If we look at our current metrics, we can see some interesting points worth investigating.

The butterfly chart on the right shows movement between customer loyalty tiers within each region of the country with the number of upgrades (on the right) and downgrades (on the left).

The vector plots show us information over multiple dimensions. These show us the difference between two values and the direction it is heading. For example, on the left, we see that Revenue is pointed downward while churns (x axis) are increasing.

The vector plot on the right shows us the change in margin from year to year as well as the customer lifetime value.

What’s interesting here is that there are two arrows that are pointing up, indicating a rise in customer lifetime value. Indeed, if we were to click on the map, we would see that these two regions are the same two that have a net increase in Loyalty Tier.

This leads me to believe that a customer’s tier is predictive of margin. Let’s investigate it further.

3. Automated Analysis

We will use the Automated Analysis feature within Visual Analytics to quickly give us the drivers of CLV.

This screenshot shows an analysis that SAS Visual Analytics(VA) performed for me automatically. I simply told VA which variable I was interested in analyzing and within a matter of seconds, it ran a series of decision trees to produce this summary. This is an example of how SAS is incorporating AI into our software to improve your experience.

Here we can see that loyalty tier is indeed the most important factor in determining projected annual margin (or CLV).

4. Influential driver

Once identified, the important driver will be explored across other dimensions to assess how influential this driver might be.

A cursory exploration of Loyalty Tier indicates that yes, loyalty tier, particularly Tier 5, has a major influence on revenue, order count, repeat orders, and margin.

5. CLV comparison models

We will create two competing models for CLV and compare them.

Here on our modeling page are two models that I’ve created to predict CLV. The first one is a Linear Regression and the second is a Gradient Boosting model. I've used Model Comparison to tell me that the Linear Regression model delivers a more accurate prediction and so I use the output of that model as input into a recommendation engine.

6. Recommendation engine

Based on our model learnings and the output of the model, we are going to build a recommendation engine to help us with determine what to do with each customer.

Represented here, I built a recommendation engine model using the Factorization Machine algorithm.

Once we implement our model, customers are categorized more appropriately and we can see that it has had an impact on revenue and the number of accounts is back on the rise!

Conclusion

Even though Customer Lifetime Value has been around for years, it is still a valuable metric to utilize in modeling and recommendation engines as we have seen. We used it our automated analysis, discovered that it had an impact on revenue, we modeled future values of CLV and then incorporated those results into a recommendation engine that recommended new loyalty tiers for our customers. As a result, we saw positive changes in overall company revenue and churn.

To learn more, please check out these resources:

How to utilize Customer Lifetime Value with SAS Visual Analytics was published on SAS Users.

11月 092018
 

In parts one and two of this blog posting series, we introduced machine learning models and the complexity that comes along with their extraordinary predictive abilities. Following this, we defined interpretability within machine learning, made the case for why we need it, and where it applies. In part three of [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 3] was published on Customer Intelligence Blog.

11月 092018
 

In parts one and two of this blog posting series, we introduced machine learning models and the complexity that comes along with their extraordinary predictive abilities. Following this, we defined interpretability within machine learning, made the case for why we need it, and where it applies. In part three of [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 3] was published on Customer Intelligence Blog.

11月 062018
 

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Using SAS at SAS: How content targeting drives better UX was published on Customer Intelligence Blog.

11月 052018
 

In part one of this blog posting series, we introduced machine learning models as a multifaceted and evolving topic. The complexity that gives extraordinary predictive abilities also makes these models challenging to understand. They generally don’t provide a clear explanation, and brands experimenting with machine learning are questioning whether they [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 2] was published on Customer Intelligence Blog.

11月 012018
 

As machine learning takes its place in numerous advances within the marketing ecosystem, the interpretability of these modernized algorithmic approaches grows in importance. According to my SAS peer Ilknur Kaynar Kabul: We are surrounded with applications powered by machine learning, and we’re personally affected by the decisions made by machines [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 1] was published on Customer Intelligence Blog.

8月 022018
 

Recently, Scott Jackson, Director Business Intelligence at the University of North Carolina at Chapel Hill shared their data quality, reporting and analytics journey. They're using SAS in a multitude of ways – from operations, institutional research, athletics – and are now looking to scale to the enterprise. They've been so successful [...]

Scaling data and analytics across the University of North Carolina was published on SAS Voices by Georgia Mariani

7月 132018
 

In part one of this blog posting series, we took an introductory tour of recommendation systems, digital marketing, and SAS Customer Intelligence 360. Helping users of your website or mobile properties find items of interest is useful in almost any situation. This is why the concept of personalized marketing is [...]

SAS Customer Intelligence 360: The Digital Shapeshifter of Recommendation Systems [Part 2] was published on Customer Intelligence Blog.

6月 132018
 

What do the New York Mets, the Orlando Magic and the Boston Bruins all have in common? They all use SAS analytics to gain deeper insights into athlete recruitment, retention, performance, safety and more. And after seeing the success teams like these have had using analytics, collegiate sports are turning [...]

The key to success in college sports? Analytics. was published on SAS Voices by Georgia Mariani

5月 182018
 

When data meets geography, use cases revolve around mapping and spatial analytics. But what happens when you combine digital analytics and powerful visualization for customer location analysis? Leveraging data collection mechanisms, SAS 360 Discover captures first-party behavioral information across the entire digital customer experience with a brand’s websites and mobile [...]

SAS Customer Intelligence 360: Location analytics meets digital intelligence was published on Customer Intelligence Blog.