Technology to Increase Customer Spend, Retention and Lifetime Value
What’s it worth to your business to increase customer lifetime value (CLV) by 20%? At least 20% to the top line and probably double or triple that to the bottom line? What’s the financial impact if you increase customer retention by 20%? And more importantly, how exactly do you increase CLV, retention and other factors that dramatically impact the company’s growth and financial performance?
These are hard questions, and pose a dilemma for most businesses.
For more than two decades CRM software has earned a reputation of over-emphasizing data capture and under-emphasizing business intelligence. Let’s face it, no business achieves competitive advantage by entering data. In fact quite to the contrary you only achieve competitive advantage by getting that data out of the system and making it actionable.
Harnessing customer behaviors in order to accurately predict how offers or incentives will influence customer response, customer spend, customer tenure and related customer impact is of strategic importance, but is also complex. Predictive analytics seeks to answer these tough questions but for many is an elusive objective that remains out of reach.
Optimove is an interesting company with a predictive analytics solution that answers these tough questions and delivers the process support so that marketers and business leaders can prioritize what metrics deliver the biggest payback to the company, and then deploy methods to achieve their objectives.
The company’s product of the same name consolidates customer data and applies an algorithm to illustrate how a marketing campaign or similar outreach will impact both the customer’s response and the company’s revenues. For many companies, this type of technology automation is the missing link needed to make 1 to 1 marketing a reality.
Here’s how it works.
Customer data such as sales, purchase and loyalty transactions are extracted using an ETL (extract, transform and load) tool. Optimove flattens the data into a common table and proprietary schema. Daily data extracts are also maintained in a way to deliver snapshots for time-lapsed reporting or trending. Once the data is captured, the system begins to create customer models along multiple dimensions and a customer life cycle continuum. For example, the system models lifecycle stages (new, active, at risk and churned customers), segmentation layers (by persona, geo, preferences, activities) and fine tunes customers into micro-segments.
This type of system generated customer modeling substitutes science for guess work and removes the substantial number of hours needed to organize customer behaviors and identify patterns which can be exercised in order to deliver more relevant messaging, offers and incentives – which then generates continued engagement and higher conversions.
Other benefits which directly contribute to customer engagement and financial objectives include:
The Optimove algorithm performs cluster analysis using RFM (Recency, Frequency, Monetary) and discovering customer personas and patterns. Applying this type of data mining to find meaningful customer segmentation is particularly valuable for retailers and B2C businesses.
Customers are grouped into segments based on behaviors across life cycle stages. This allows marketers to identify like customers and predict what those early stage personas will do, or can be influenced to do, based on what late stage customers in the same persona group have already done. This type of predictive behavior is extremely powerful when creating marketing campaigns, defining lead scores and prioritizing which accounts to pursue.
Retention marketing. When you understand how customers evolve, you can proactively respond in a way that caters to customers. Customers don’t stand still. They evolve, change preferences, move from one segment to another and churn. Understanding and predicting customer behaviors is a dynamic and fluid process — which explains why so few businesses are able to tap into this opportunity and why software automation is required. By understanding, predicting and facilitating customer evolution, smart businesses can better engage customers, influence spend behavior, grow customer share, increase CLV and keep customers longer.
The application calculates Customer Lifetime Value and can forecast the financial impact from increases in CLV based on history with like personas, segments and micro-segments. By knowing your CLV you also know exactly how much a lead is worth and how much you should be willing to spend to acquire new customers or keep existing customers. Many businesses track customer metrics such as profit per customer because they’re easy to calculate. Unfortunately, these metrics are all historical. Performance measures such as CLV are forward looking, and even a small change to CLV can have an extraordinary impact to the business over many years.
The best learning comes from testing. Marketing best practices often provide a good starting point, but the best marketing techniques are only derived from actual experience with your company’s target market and customers. This application divides campaign populations into A/B sets and uses control groups in order to calculate whether any particular variable resulted in a statistically significant result. Uncovering these marketing variables means the difference between mediocre and best in class marketing performance.
Using a data mining approach to dynamically predict which marketing actions will be the most effective for each individual customer increases repeat purchases, retention and lifetime value, and is a game changer for most companies.
So, what’s it worth to your business to increase customer lifetime value by 20%? Maybe it’s time to finally answer this question. And based on the answer, maybe it’s time apply technology to make it happen.
Share This Article
By understanding, predicting and facilitating customer evolution, smart businesses can better engage customers, influence spend behavior, grow customer share, increase CLV and keep customers longer.