In this post we look at solving the critical business problem of managing the customer buying cycle, and how tools — simple to advanced — can help sales people support their customers.
In a repeat run rate business, customers are buying many categories of products and replenishing supplies and inventory on a weekly basis. With so many transactions from customer orders that reps must contend with, it is easy to forget about the customers who are NOT ordering, who are ordering less or not ordering the core high-value products any more.
While we are a predictive analytics company and have a solution to offer here, we also explore with customers and prospects who are starting out this question: What is the right fit for their needs? Here are three options in the order of features, scale and cost, with pros and cons we’ve discussed with them:
SALES PROCESS SOFTWARE
Primarily focuses on mechanically guiding rep actions through a rules engine: “call today … email next week … follow-up in 3 days.” Stand-alone or blends with CRM.
Pros: Most simple/easy to follow, ideal for a small team within a larger sales force.
Cons: Does not consider customer attributes and responses or historical sales data, and is not predictive.
MARKETING AUTOMATION SOFTWARE
Tracks frequency of contacts for communication, which is another way to stay on top of the buying cycle.
Pros: Maps to a pre-thought-out contact strategy, multi-channel, captures customer preferences and responses, and increasingly sophisticated. Ideal for 50-100 reps.
Cons: No historical sales data used, is not predictive, cannot blend external data, may not incorporate sales rep actions, does not identify cause and effect.
PREDICTIVE ANALYTICS SOFTWARE
This niche fits what we do. It is the most costly of the three because of data consolidation, involving expert statisticians with domain expertise, etc. But this process can scale indefinitely, it is predictive, and it can prove generation of incremental sales.
Pros: Support multiple sales channels, extensive data crunching, most accurate, provides cause/effect drivers. Ideal for 200+ reps, integrates with CRM seamlessly, best ROI.
Cons: Generally more expensive, more time to calibrate and start using, involves more people and technology (need expertise).
So there you have it. While we sell the hammers, aka predictive analytics software, we don’t see every problem as a nail that needs to be pounded. These tools all have a place in the sales arsenal, so take the time to determine what’s right for your organization.
Customer life stage is the standard bearer of segmentation. Customer stages like “new,” “active,” “lapsed” and “lost” serve an important purpose by grouping customers into homogenous, manageable clusters for marketing, value measurement and investment decisioning. However, these definitions have limitations that you should consider and correct prior to sales applications.
Here are key limitations and how to overcome them.
Measurement windows are too broad: Most of these segments have a 6- or 12-month horizon for comparison. A “new” customer stays in that segment for 6 months, often regardless of spend or activity, whether based on total spend or number of orders. Similarly, a “lost” customer is usually defined as any spend in the past 13–24 months, but zero spend over the past 12 months. As you can see both the time and revenue windows are very large, and that dilutes usefulness. Regarding time, this can be a few weeks or up to 24 months, and regarding revenue, the measure can be a few hundred dollars to several thousand.
Corrections: Create smaller segments for sales applications with sub-definitions. These can be arbitrary to follow a business threshold (i.e., 6 months or $2,000) or they can segment eligible customers into equal percentages, like 50-50 or 33-33-33 percent.
Another option is to create “run-rates” based on a larger window, but track smaller increments. For example, if “lost” customers are tracking 30% over 12 months, use a rolling 3-month window and 7.5% as the expected rate for this time window. Over time, you will arrive at a number that works for you.
Lack of interactivity: Once you have defined customer segments, customers are targeted for campaigns based on the static view. However, from a sales perspective, many factors influence customers’ migration from one segment to another. The quality of the rep (overall experience including with company, performance level), quality and quantity of the interaction, and tenure of the rep with the customer are all critical to influencing customer behavior.
Plus social media plays an increasing role in gauging customer sentiment. Social media gives many options for an alert sales force to listen and reach out proactively to customer needs.
Corrections: Allow for scoring within each segment based on interactions, and then have reps follow-up with customers. In the catalog world, it has long been known that product returns are actually a top predictor of repeat purchase. That hardly deserves a sales call, but for an early-stage customer this could be a significant differentiator to accelerate them into an active buying cycle.
Also by creating sub-segments, sales reps can initiate conversation with smaller changes in customer behavior, which leads to a more timely conversation.
Bias towards past or present value: All these segment definitions are based on what is already known about customers, not what is expected. With new customers, attempting to estimate their next 6- or 12-month value will be very helpful so you can take immediate actions rather than wait for customers to “prove themselves.” A placebo effect might create a self-fulfilling prophecy and grow value among new customers, but this is not likely to be the norm.
In the case of lapsed customers, the reverse is true. The emphasis is on the current value and less emphasis (or none!) on where the customers were 12-24 months ago. Thus customers who peaked much higher tend to get treated similar to customers who may not have such potential or share of wallet.
By looking at the past – or a single point – of value, trends also can be missed.
Corrections: Create future value measure for each outcome. Identify early stage customer trends to track and nurture new customers. When looking at retention, add “weights” for peak spend to give former high-value customers more priority.
Using two points of customer value allows you to capture trending info, and this is another way to differentiate among customers with an eye towards future value.
Set up a method you’re comfortable with that produces a future value. This can be simple up/down metric or a more elaborate statistical model. Within each life cycle segment, make a two- or three-way split of customers based on estimated future value. Have sales reps suggest and contribute additional metrics tied to their performance. For example, deeper category penetration as a goal with the early-stage customers.
In closing, for customer segmentation to bring better ROI for sales, three elements must be in place:
Actionability should be tactically focused with short windows
Have a future value orientation
Ensure that reps take responsibility for their customer interactions. Getting sale reps’ input, recognizing their workload/behaviors, and providing a long-term perspective can enhance the value of segmentation for the benefit of sales.
This post was inspired by a recent entry on The Sales Blog that discussed how it is easy to do the enjoyable things, when instead we should focus on things needed to improve sales results. Such as, resolving customer problems. Problems with a customer don’t go away, unless the customer goes away.
So from our quantitative perspective, how would we suggest making it easier for salespeople to engage in difficult conversations? Three ways this could be done:
Mix up the servings. Segment your customer portfolio based on buying cycle. Ensure that the salesperson is calling on all segments in some proportion that reflects both gains in immediate sales and long-term relationship. Use the CRM system to set up call blocks that are driven by analytics, to ensure consistent actions across the entire sales force.
Demonstrate the loss from not making the call. Salespeople hate leaving money on the table. To show how much could be left behind, define a customer segment to call first as suggested above. Then identify actual sales revenue from sales calls made previously to a similar customer segment. Use this revenue figure to establish a per-customer baseline of incremental sales generated. Multiply this figure by number of customers in your “to call first” segment. Use this to show sales reps how much revenue may be lost by not calling. You can also extend this lost revenue estimate into a longer time horizon, showing revenue lost from what would have been future repeat purchases.
Remove the responsibility. Give the salesperson a time limit or maximum number of attempts to make the call to a priority customer identified by the analytics. For example, consider a program that reaches out to lapsed customers. This is often a difficult conversation – the rep knows the customer was a great customer at one time, the rep did not keep track and call proactively, and the customer moved to a different supplier. By removing this account from the rep’s portfolio, the account can be put into a nurturing program, given to newer reps who are more hungry, or other approaches for a fresh start.
The Sales Blog article speaks about salespeople enjoying taking customers out to ballgames. So we’ll leave our readers with this thought: when treated as a defensive strategy to keep a failing account, we have found that this socializing approach actually backfires. After the event, most customers subsequently curtail further or reduce spend to zero with the company. We were very puzzled – could this fun outing actually be a catalyst to losing the account? Because most likely, the difficult conversation to square with the customer upfront did not happen.
Just like the old proverb, “a stitch in time saves nine,” there is a right time for these necessary conversations. Predictive analytics can help detect patterns that identify when that first stitch is required before the relationship is torn beyond salvage.
I was rooting for our hometown Chicago Bears team in the NFC championship game last Sunday night. Guess that didn’t work out so well. We learned the value of backups aka deep bench, but it wasn’t enough to save the game.
I watched the game while working on a customer retention project, so I couldn’t help but see parallels between retaining a transactional customer and the wide receiver catching the ball on the deep passes. After a customer places an order, the customer is like the ball out of the QB’s hands as it soars above the field to its intended destination.
But as the saying goes, “When you throw a ball, three things can happen and two of them are bad.”
Here is where the customer correlation comes in. Just like the ball must come back to earth, the customer must buy again. On the bad side:
your competition intercepts (gets the order at the right time) or
you did not anticipate and get to the position at the right time or simply fumbled (did not make proactive calls or relevant upsell/cross-sell offers).
Why? Other priorities can get in the way or there is poor communication between teams, systems or processes. The result is the ball gets dropped, literally and figuratively.
On the field, the best combination of planning, tools and execution leads to anticipation to be in the right place at the right moment to catch the ball, assuring the next first and ten. In sales, anticipation is about predicting the customer buying cycle and having call scripts, plays or promotions geared to that point in time. Use analytics and CRM systems to set up these actions so that you are running in the right direction.
At Valgen, we don’t ask the question “when is the customer going to leave?” Instead we try to predict, “when is the customer going to buy?” The customer’s buying cycle dictates the size and scope of their needs. If we predict that a follow-on order is expected – sort of like a short pass – don’t wait for setting up promotions. Just act. On the other hand, if a larger order with a long lead time is expected – a Hail Mary pass perhaps? – use the “in-buying cycle” time to set up a more coordinated play bringing more resources to bear.
My best wishes to the Packers and Steelers at the Super Bowl. But you know, I’ll be cheering for the Packers anyway… we’re closer neighbors after all.
Your lead generation machine is working tirelessly to bring in new leads and appointments. Marketing keeps hot prospects focused on the message and proposition. Your sales team has done an excellent job of converting them into a first time buyer.
And then something not-so-good happens. The shiny new customer does not come back to make a second purchase. Months go by, and they end up in this no man’s land called “one time buyers.” As a lead, they are coveted. As a buyer they are ironically abandoned. Countless artists have sung the “one and done” blues. Stevie Wonder even has a top hit on this, I think.
True cost of leads
The consequence of underperforming leads is enormous – from metrics such as cost per lead, multi-buyer conversion, average initial sale to cost per buyer. Indications are that a hidden deficiency to turn first-time customers into repeat buyers seriously understates the true cost of customer acquisition. Lead generation should not stop with the initial sale, rather it should cultivate prospects that contribute to maximizing long-term franchise value. This can only happen through continuing repeat purchases and higher level of engagement.
It’s a safe bet that you do not make money on the first sale after subtracting net profit from the cost to acquire the customer. When you amortize the cost of up-front lead generation costs, consider adding another metric so you can measure against cost to make two purchases, three purchases, five purchases and so on. Given the incremental profitability of repeat purchases, it should come as no surprise that the ROI becomes exponential.
What you can do
So how can you reap the benefits of this promise? Identify the symptoms, then interpret the signals and take decisive corrective action so these leads never slip into the no man’s land. Let us discuss each of these stages in detail.
First, identify how many customers have made only one, two or three purchases. Look at this in time intervals of six, twelve and twenty-four months since initial purchase. Also perform a vintage analysis by quarter for the past two years.
Second, look at the purchase details. Were most customers buying just accessories? Were they rush shipments? Were products only in one product category? Only on sale? Online or offline? Are some sales teams more prone to the “one and done” problem than others? By segmenting these buyers, you will begin to get a good idea of what is producing the majority of these customers.
Finally, put in place a campaign or process to touch these customers early. Inquire about their buying experience. Look at an attach that you could drive immediately. Set multiple touches based on the first order amount, profitability of the product purchased, or time since the initial purchase. Offer a value added service that “welcomes to the organization.”
There are many reasons why a customer may not make a second purchase. But a significant reason often is that there is no follow-up. By establishing a proactive process, you can extend the ROI of lead generation significantly through simple steps that resonate with the shiny, new customer.
Photo credit: Chicago Mayor’s Office. “One size fits all” is often not the most flattering. Here 4,436 people wear the same Groucho Marx glasses at one time, winning a 2009 Guinness World Record for Chicago.
We’ve heard phrases like “call often,” “call everybody,” and “call until you get an order” espoused as sales and marketing strategies. But we should evaluate whether this approach produces the best ROI, particularly when cost per call is high. This is a key issue with sales productivity.
Outbound calling has direct costs of the rep’s time, mileage and/or telecom utility costs, indirect overhead costs, and the opportunity cost of not calling a customer who would have ordered instead. Let’s expand on the last component and explore its role in determining sales productivity.
Sales 2.0 is about nurturing and facilitating the buying cycle, even in a transactional sales model. Your B2B customer is buying what they need – technology products, office supplies and medical supplies for example – driven by the demand for their services and sales needs, not from their benevolence to your sales person, to borrow from Adam Smith the pioneering economist.
In other words, a company that is larger, expanding faster, and experiencing increased end-consumer demand is simply going to buy more often. So when we apply an average – which is what a “call often” mandate implies – we aren’t putting the customer’s buying cycle first. This doesn’t respect the customer’s buying cycle.
Let’s see an example of how this can adversely affect your sales. Assuming an average two-month buying cycle, take a look at this contact pattern:
Clearly, the fallacy of “one size fits all” becomes immediately obvious. “One size fits all” is usually not a good fit, for anything in life.
Further, overlay the fact that even for a single customer, the buying cycle may change from one order to the next, such as when the customer:
Placed a one-time sizable order because of year-end, or seasonal effect
Bought a number of items but now needs to follow-up with related products
Buys product categories with different replenishment rates
Other factors (marketing offers) or lower-than-anticipated sales
Sales organizations that have the ability to read each customer’s unique buying cycle and make contact at the most opportune time are typically rewarded with higher probability of a sale, higher add-on sales, penetration in more categories and higher margin sales. This is not only valuable to the seller, but provides better customer service and increased customer loyalty. Let’s not forget the decreased fatigue on the part of both customer and sales person from avoiding undesired contact. It’s the proverbial “strike while the iron is hot,” which after all, only makes sense.
So instead of “call often,” think “call at the right time.”
Anticipating each customer’s buying cycle requires a good read of past data, trending based on current events, relative buying behavior, and sales and marketing stimulus. Tools like predictive modeling certainly make this more accurate. Segmenting customers based on their individual buying cycle and devising proper sales contact frequency is a start. We would be even better served if all sales and marketing initiatives revolved around the customer’s buying cycle.
When a sales team shoots from #12 to #2 in company rankings, people ask questions. Wouldn’t you?
This team’s sales gain was the equivalent of getting an extra month of revenue. But without the extra month of effort squeezed into a fiscal year.
The team was using predictive analytics, but they didn’t really know it. What they knew was, every day they got a list of customers to contact. And they contacted them. And they sold more. The customers seemed ready, receptive.
The sales team was happy — not only did they get congratulations, they got commissions. Other teams took quick notice and wanted the advantage too. And it spread from there across 2,000 reps. What was it? It was cftime.
It was very gratifying to see true results happen for people. Much gain, without pain. Seeing people benefit from predictive analytics is our passion. That’s why we want to see more sales teams discover what it can do for them. That’s why we started Valgen. That’s also why we started this blog, to share more with you.
In this post we look at solving the critical business problem of managing the customer buying cycle, and how tools — simple to advanced — can help sales people support their customers.
Measurement windows are too broad: Most of these segments have a 6- or 12-month horizon for comparison. A “new” customer stays in that segment for 6 months, often regardless of spend or activity, whether based on total spend or number of orders. Similarly, a “lost” customer is usually defined as any spend in the past 13–24 months, but zero spend over the past 12 months. As you can see both the time and revenue windows are very large, and that dilutes usefulness. Regarding time, this can be a few weeks or up to 24 months, and regarding revenue, the measure can be a few hundred dollars to several thousand.
Lack of interactivity: Once you have defined customer segments, customers are targeted for campaigns based on the static view. However, from a sales perspective, many factors influence customers’ migration from one segment to another. The quality of the rep (overall experience including with company, performance level), quality and quantity of the interaction, and tenure of the rep with the customer are all critical to influencing customer behavior.
Bias towards past or present value: All these segment definitions are based on what is already known about customers, not what is expected. With new customers, attempting to estimate their next 6- or 12-month value will be very helpful so you can take immediate actions rather than wait for customers to “prove themselves.” A placebo effect might create a self-fulfilling prophecy and grow value among new customers, but this is not likely to be the norm.
This post was inspired by a recent entry on 
I was rooting for our hometown Chicago Bears team in the NFC championship game last Sunday night. Guess that didn’t work out so well. We learned the value of backups aka deep bench, but it wasn’t enough to save the game.
