DESCRIPTIVE ANALYSIS

Life-Time Value is typically done as just a calculation, using past (historical) data. That is, it’s only descriptive.

While there are many versions of LTV (depending on data, industry, interest, etc.) the following is conceptually applied to all. LTV, via descriptive analysis:

1)Uses historical data to sum up each customer’s total revenue.

2)This sum then has subtracted from it some costs: typically cost to serve, cost to market, maybe cost of goods sold, etc.

3)This net revenue is then converted into an annual average amount and depicted as a cash flow.

4)These cash flows are assumed to continue into the future and diminish over time (depending on durability, sales cycle, etc.) often decreasing arbitrarily by say 10% each year until they are effectively zero.

5)These (future, diminished) cash flows are then summed up and discounted (usually by Weighted Average Cost of Capital) to get their net present value.

6)This NPV is called LTV. This calculation is applied to each customer.

Thus each customer has a value associated with it. The typical use is for marketers to find the “high valued” customers (based on past purchases). These high valued customers get most of the communications, promotions / discounts, marketing efforts, etc. Descriptive analysis is merely about targeting those already engaged (much like RFM).

This seems to be a good starting point but, as is usual with descriptive analysis, contributes nothing about WHY. Why is one customer more valuable, will they continue to be? Is it possible to extract additional value, but at what cost? Is it possible to garner more revenue from a lower valued customer because they are more loyal or cost less to serve? What part of the marketing mix is each customer most sensitive to? LTV (as described above) gives no implications for strategy. The only strategy is to offer and promote to the high valued customers.

PREDICTIVE ANALYSIS

How would LTV change using predictive analysis instead of descriptive analysis? First note that while LTV is a future-oriented metric, descriptive analysis uses historical (past) data and the entire metric is built on that, with assumptions about the future applied unilaterally to every customer. Prediction will specifically thrust LTV into the future (where it belongs) by using independent variables to predict the next time until purchase. Since the major customer behavior driving LTV is timing, amount and number of purchases, a statistical technique needs to be used that predicts time until an event. (Ordinary regression predicting the LTV amount ignores timing and number of purchases.)

Survival analysis is a technique designed specifically to study time until event problems. It has timing built into it and thus a future view is already embedded in the algorithm. This removes much of the arbitrariness of typical (descriptive) LTV calculations.

So, what about using survival analysis to see which independent variables, say, bring in a purchase? This decreasing time until purchase tends to increase LTV. While survival analysis can predict the next time until purchase, the strategic value of survival analysis is in using the independent variables to CHANGE the timing of purchases. That is, descriptive analysis shows what happened; predictive analysis gives a glimpse of what might CHANGE the future.

Strategy using LTV dictates understanding the causes of customer value: why a customer purchases, what increases / decreases the time until purchase, probability of purchasing at future times, etc. Then when these insights are learned, marketing levers (shown as independent variables) are exploited to extract additional value from each customer. This means knowing that one customer is say sensitive to price and that a discount will tend to decrease their time until purchase. That is, they will purchase sooner (maybe purchase larger total amounts and maybe purchase more often) with a discount. Another customer prefers say product X and product Y bundled together to increase the probability of purchase and this bundling decreases their time until purchase. This insight allows different strategies for different customer needs and sensitivities, etc. Survival analysis applied to each customer yields insights to understand and incent changes in behavior.

This means just assuming the past behavior will continue into the future (as descriptive analysis does) with no idea why, is no longer necessary. It’s possible for descriptive and predictive analysis to give contradictory answers. Which is why “crawling” might be detrimental to “walking”.

If a firm can get a customer to purchase sooner, there is an increased chance of adding purchases–depending on the product. But even if the number of purchases is not increased, the firm getting revenue sooner will add to their financial value (time is money).

Also a business case can be created by showing the trade-off in giving up say margin but obtaining revenue faster. This means strategy can revolve around maximization of cost balanced against customer value.

The idea is to model next time until purchase, the baseline, and see how to improve that. How is this carried out? A behaviorally-based method would be to segment the customers (based on behavior) and apply a survival model to each segment and score each individual customer. By behavior is typically meant purchasing (amount, timing, share of products, etc.) metrics and marcom (open and click, direct mail coupons, etc.) responses.

AN EXAMPLE

Let’s use an example. Table 1 shows two customers from two different behavioral segments. Customer XXX purchases every 88 days with an annual revenue of $43,958, costs of $7,296 for a net revenue of $36,662. Say the second year is exactly the same. So year 1 discounted at 9% is NPV of $33,635 and year 2 discounted at 9% for two years is $30,857 for a total LTV of $64,492. Customer YYY has similar calculations for LTV of $87,898.

TABLE 1 | ||||||||||

CUSTOMER | DAYS BETWEEN PURCHASES | ANNUAL PURCHASES | TOTAL REVENUE | TOTAL COSTS | NET REV YR 1 | NET REV YR 2 | YR1 DISC | YR2 DISC | LTV AT 9% | |

XXX | 88 | 4.148 | $43,958 | $7,296 | $36,662 | $36,662 | $33,635 | $30,857 | $64,492 | |

YYY | 58 | 6.293 | $62,289 | $12,322 | $49,967 | $49,967 | $45,842 | $42,056 | $87,898 |

The above (using descriptive analysis) would have marketers targeting customer YYY with > $23,000 value over customer XXX. But do we know anything about WHY customer XXX is so lower valued? Is there anything that can be done to make them higher valued?

Applying a survival model to each segment outputs independent variables and shows their effect on the dependent variable. In this case the dependent variable is (average) time until purchase. Say the independent variables (which defined the behavioral segments) are things like price discounts, product bundling, seasonal messages, adding additional direct mail catalogs, offering online exclusives, etc. The segmentation should separate customers based on behavior and the survival models should show how different levels of independent variables drive different strategies.

Table 2 below shows results of survival modeling on the two different customers that come from two different segments. The independent variables are price discounts 10%, product bundling, etc. The TTE is time until event and shows what happens to time until purchase based on changing one of the independent variable. For example, for customer XXX, giving a price discount of 10% on average decreases their time until purchase by 14 days. Giving YYY a 10% discounts decreases their time until purchase by only 2 days. This means XXX is far more sensitive to price then YYY–which would not be known by descriptive analysis alone. Likewise giving XXX more direct mail catalogs pushes out their TTE but pulls in YYY by 2 days. Note also that very little of the marketing levers affect YYY very much. We are already getting nearly all from YYY that we can, no marketing effort does very much to impact the TTE. However, with XXX there are several things that can be done to bring in their purchases. Again, none of these would be known without survival modeling on each behavioral segment.

Table2 | ||

xxx | yyy | |

VARIABLES | TTE | TTE |

price discount 10% | -14 | -2 |

product bundling | -4 | 12 |

seasonal message | 6 | 21 |

5 more catalogs | 11 | -2 |

online exclusive | -11 | 3 |

Table 3 below shows new LTV calculations on XXX after using survival modeling results. We decreased TTE by 24 days, by using some combinations of discounts and bundling and online exclusives, etc. Note now the LTV for XXX (after using predictive analysis) is greater than YYY.

TABLE 3 | ||||||||||

CUSTOMER | DAYS BETWEEN PURCHASES | ANNUAL PURCHASES | TOTAL REVENUE | TOTAL COSTS | NET REV YR 1 | NET REV YR 2 | YR1 DISC | YR2 DISC | LTV AT 9% | |

XXX | 64 | 5.703 | $60,442 | $10,032 | $50,410 | $50,410 | $33,635 | $30,857 | $88,677 | |

YYY | 58 | 6.293 | $62,289 | $12,322 | $49,967 | $49,967 | $45,842 | $42,056 | $87,898 |

What survival analysis offers, in addition to marketing strategy levers, is a financial optimal scenario, particularly in terms of costs to market. That is, customer XXX responds to a discount. It’s possible to calculate and test what is the (just) needed threshold of discounts to bring a purchase in by so many days with the estimated level of revenue. This ends up being a cost / benefit analysis that makes marketers think about strategy. This is the advantage of predicative analysis–giving marketers strategic options.