Identifying and understanding your high spenders and frequent shoppers is vital to maintaining and improving your revenue stream, but if your customer base is large (on the order of several thousand customers), manually sifting through customer data to recognize the patterns can be costly and time consuming. Machine learning can prove a viable solution to this problem – not only by removing the effort associated with combing through customers, but by exposing previously unseen relationships in your data to drive decision making. In this blog, we will uncover benefits to leveraging machine learning for customer analytics, and two examples of use cases - Customer Lifetime Value and Customer Segmentation - ripe for machine learning.
To start, let’s define what your “best” customer looks like. The specifics will vary by business, but all customers have one thing in common: they buy things. In general, a good customer is one who buys often, spends a lot, and intends on continuing this activity over time. As long as we are recording who buys what and when, then we have enough information to begin separating the best customers from the rest. With knowledge of who these customers are and their key characteristics, we can look at what the best customers have in common and devise ways to target and acquire more.
There are many different machine learning algorithms that can help us automate the analysis. The decision of which to use can differ based on the data available and what is most important to your business, but they all work in the same general way by continuously iterating to adjust model parameters and improve accuracy. There are many benefits of using machine learning models:
The output of the models can be used to automate decision making processes that would normally take weeks or months. For example, if your organization needs to decide “is it actually worth it to send out coupons to this subset of our customers?” Automating this decision could entail establishing cutoff points based on a customer's propensity to engage and price sensitivity to maximize the value of the couponing program.
There are two commonly used algorithms that will solve this business question. One type of model that generates a lot of interest is a customer segmentation model. A segmentation model takes the data you have and separates customers into segments that have similar characteristics. Because there is no specific value we are trying to predict, the analyst and business users work together to identify what models are both statistically significant and actionable for the business.
The K-means clustering algorithm is an example of a commonly-used segmentation model. It starts by randomly defining groups and incrementally updating them so similar customers end up being grouped together. The result is a handful of groupings that can be investigated further to identify what the segments’ defining characteristics are. Here is an example where we are segmenting customers with machine learning based on their average purchase size and income.
Another commonly used model is a “Customer Lifetime Value” model (CLTV). CLTV models predict the expected value that will come from each customer. At their simplest, they can use data speaking to purchasing behavior (last purchase date, number of purchases, and money spent) to predict what we expect each customer to spend over a specified time period. At their most advanced, they can include referral and cost to maintain, as well as how much customers will spend themselves. Once you know how much a customer’s spending is worth to your business, then you know what you may be willing to spend in marketing and other costs. When paired with a clustering model, the projected value can be used to see which segments and characteristics are most or least valuable to your business.
Machine learning can seem overwhelming, and if you haven’t gotten started with its implementation, you are certainly not alone. Many companies haven’t taken their first steps simply because they don’t know where to begin. For others, it’s seen as something you can tackle when you’ve “made it” with your data and analytics strategy. These concerns are understandable, but getting value from machine learning may be much easier than you think.
You probably already have data on transactions: customer, product, location, time, and price. As long as you have the basics, you have enough for a simple customer analysis, and that analysis can evolve over time as you collect more data.
Written by CCG, an organization in Tampa, Florida, that helps companies become more insights-driven, solve complex challenges and accelerate growth through industry-specific data and analytics solutions.
CCG understood our project needs very well, they are very responsive and we could not ask for anything more. The solution they provided fit perfectly with our expectations and business goals.GOP Data TrustChief Data Officer
I cannot overstate the delight we experienced from the outcome of our project. I would not only recommend CCG to any company, but question why they would engage with anyone but CCG.PgiDirector of Customer Success
Working with CCG is like working with extended team members. Consultants become an integral part of the work bringing expertise for cutting edge design and development.Hillsborough County Public SchoolsChief Information and Technology Officer
CCG's team is positive and eager. They are a great big bunch of wonderful people trying to make a difference.Hillsborough County Public SchoolsDepartment Manager
I knew CCG's technical expertise and dedication to quality results would be invaluable to our project success based on our past partnerships. We could not have implemented in the short timeframe like we did without their assistance. CCG is #1 on my speed dial for successful project implementation.InCommDirector, Financial Information Systems
It was evident from the onset of negotiations through the implementation that CCG took their role in the partnership to heart and we believe it has been instrumental in our success.Interval InternationalDirector of Marketing
CCG works very hard to understand and align with our needs. It truly feels as though we are on the same team!Fortune 500 HomebuilderBI Manager
CCG came to our company in a time of much change. Their team partnered with ours, continually delivering with professionalism and efficiency. We would not be where we are today without the expertise CCG brought to the project.PSCU Financial ServicesSenior Program Manager
CCG has a good industry knowledge, we are very happy that we chose to work with CCG. They have been a great help strategically and are helping us make important decisions.Minneapolis Public SchoolsHuman Capital Coordinator
Other Vendors use the word Partnership, but CCG actually means what they say. I can’t thank them enough for their professionalism and willingness to work with us as a true Partner, not just another vendor.PODSCIO
Our CCG Consultants are total rock stars: very thorough with a solid knowledge of the financial services industry. As a bonus, they are very easy to get along with – a great fit for our team.Raymond James Financial ServicesSenior Manager of Enterprise Data
CCG's team are all amazing. Thank you, CCG, for all that you do to make us great and keep our credit unions moving forward!PSCU Financial ServicesVP Enterprise Analytics & BI
Other Vendors use the word Partnership, but CCG actually means what they say. I can’t thank them enough for their professionalism and willingness to work with us as a true Partner, not just another vendor.PODSChief Information Officer
CCG's Team is very professional and responsive. They are making our job very easy.Rollins, Inc.Senior BI Analyst
CCG did an excellent job! Their team was very flexible. They gave us everything we asked for and then some.Rooms To GoSenior BI Architect
I'm amazed at the talent at CCG, not just the skillset - they're really good people. We've already referred them once and will do so again!Ruth's Chris Hospitality GroupCIO
CCG did a great job! We're extremely impressed with what was built in a short time. CCG has delivered ahead of time and with best practices, it's been a pleasure to work with them.VologyVP of Analytics
2502 N. Rocky Point Drive, #650, Tampa, FL 33607
Phone: 813.968.3238 | Fax: 813.200.1357
8000 Avalon Blvd. Suite #100, Alpharetta, GA
Phone: 404.328.7298 | Fax: 813.200.1357