The organic growth opportunity for large B2B companies is greater than ever, where it’s common to have tens of thousands of products and customers.
But the massive complexity at this scale makes it impossible for sales reps to closely track each customer and, thus, they miss key purchase signals such as a drop in volume in a given product category. Their books of business are simply too large to keep up with customer buying patterns to detect defections in every account. As a result, churn remains the silent killer of growth.
In fact, it’s six to seven times more expensive to acquire a new customer than it is to keep a current one, according to Bain & Company. Once you acquire a new customer, without continued focus, the customer slowly begins reducing its purchase volume and, eventually, defects. Most businesses know that customer churn can devastate revenue but finding an effective means to combat churn remains elusive.
So it’s no surprise that 49% of respondents to PricewaterhouseCooper’s 16th Annual Global CEO Survey identifies organic growth as the top pursuit for growth in coming years.
The stats reveal what most businesses already know – a fresh approach to organic growth is needed. To that end, 82% of respondents anticipated significant changes to their existing strategies on customer growth and retention.
Manual analysis can’t keep up with massive complexity
Spotting defection when it begins seems easy enough for one customer-product combination. However, in a massively complex environment, most sales reps are forced to spend 80% of their time on their top 20 customers and their top products, and take a reactive approach to managing the remaining long tail. Identifying when each and every customer starts dropping volume on every product line is impossible through manual analysis.
To help reps stay on top of all their accounts, companies employ a mix of spreadsheets, business intelligence tools and a team of analysts to review past transactions and produce reports for the sales teams. However, there are several reasons why this approach fails, including the following:
A. Salespeople aren’t analysts. Sales reps don’t have the bandwidth or motivation to read, analyse and draw actionable insights from these reports. Your top sales reps may take advantage of the reports but a majority of sales reps will simply ignore them.
B. Reports are backward looking. The reports can tell reps what their customers purchased in the past but they don’t provide explicit guidance as to which customers and products reps should focus on in the future. They also fail to predict defection based on early indicators. It’s certainly helpful to understand your historical customer data but a backward looking report won’t help employees make better decisions in the future.
C. Manual approaches can’t scale. Even if you build a great analytics system, it will likely require hundreds of analysts to manage the level of complexity present in your business and perform thorough analysis on each and every customer and product combination in your book of business on a weekly, or even monthly, basis. The time-and labor-intensive effort to produce the reports typically proves futile when it comes to boosting revenue.
The reality is that the business intelligence tool and manual analyst approach crumbles under such massive complexity, causing sales reps to guess the best places to spend their time and continue making decisions based on intuition alone. Without a clear path to combat churn, B2B companies continue to follow the same stale tactics and continue to yield the same stale results.
Customer churn by the numbers
Churn is so widespread and incremental that even small dips in volume quickly add up to significant amounts of lost revenue, working against organic growth goals. To see how this happens, let’s look at the case of a single customer at a large distributor based in the UK. A mid-tier customer, in terms of spend, might account for £10,000 in revenue quarterly. The customer purchases a varying combination of 50 products. After working hard to win this account, the sales rep focuses his time on other net-new acquisition opportunities.
With less attention from the sales rep, competitive offers begin to entice the buyer. At first, it’s just a few products lost to competitors; quarterly spend drops to £8,000. The next quarter, this customer drops a few more products; spend drops to £6,000. The rep eventually notices the change but it’s too late; the customer has defected. The distributor not only lost the annual revenue, but the sales rep will have to work harder and perhaps offer larger discounts to win the customer back.
While some churn is acceptable, it is a much larger problem than most companies will admit. In fact, we’ve found that at any given time, 20% to 40% of a typical B2B company’s customer base is churning. As a result, sales reps are forced to make up for lost revenue before they can even begin contributing to organic growth.
Many B2B companies struggle to deploy a strategy effective enough to overcome the negative impact of churn, but there are alternative methods to the complex and ineffective routine of manual analysis, such as the use of predictive sales models.
Preventing churn with predictive models
Sales teams can now rely on predictive models to drive growth in their existing customer base and stay ahead of customer churn. The key to reducing churn is identifying when a competitor first starts taking business from you. Using predictive algorithms and advanced science to analyse existing customer data, companies can predict when a customer is beginning to defect and find opportunities for wallet-share expansion.
Rather than relegating decisions about which customers to call on and which products to talk about to guesswork, you can guide your sales reps to the best opportunities for revenue growth and biggest risks of defection. By detecting customer churn with predictive analysis, sales teams still have time to pre-empt it, which means companies don’t have to “buy back” the business later. With an early warning system, sales reps also have the opportunity to introduce new categories, product lines and stock-keeping units. They can spend more time doing what they do best: growing customer relationships and winning more business.
Furthermore, these cross-sell and retention opportunities can be delivered directly to sales people, in the tools they already use today, as actionable sales opportunities at the product category- and customer-level, rather than a report that needs to be deciphered. The use of algorithms and predictive models also takes care of the scalability problem that many companies face with manual approaches.
It’s clear that a new sales force is emerging – one that should be technically, socially and skilfully diverse. Even the most experienced sales teams will need data-driven, predictive sales guidance to ensure success. It’s time to bring some science to the art of selling.
About the author: Phil Anderson is vice-president of sales for the Europe, Middle East, Africa, and Asia Pacific markets at Zilliant.