Fixing Sales Problems: Will Your Sales Decisions Drive Profitable Growth or Are They Doomed to Fail?
Fixing Sales Problems: Will Your Sales Decisions Drive Profitable Growth or Are They Doomed to Fail?
We have all been in those meetings where we need to fix a sales problem. Everyone in the room has an opinion based on their experience and training on fixing the problem. The trouble is that our gut and intuition are biased far too often, and they are not enough today. When we use bias to solve complex problems, we rarely solve them entirely, resulting in another meeting or two.
We have experienced more change and disruption over the past 24 months than ever before.
Can we trust our gut and intuition, or is it time to make data-driven decisions?
Could what we have always counted on, our experience and intuition, be compromised by biases?
Could those biases cause our strategies to be doomed from the start?
What data can we count on to make strategic sales decisions?
The purpose of this post is to establish that cognitive bias exists in sales decision-making and fixing sales problems. However, with clean, actionable data and thorough analytics, the risks of sales decisions that negatively impact the bottom line are considerably reduced.
When I was the Managing Director of Pragmatic Marketing, we gave customers coffee mugs that read
“Your opinion, although interesting, is irrelevant.“
We provided product management and marketing training for leading companies throughout the world. We wanted to reinforce everyone has opinions, but we must use current market data and requirements to shape your growth strategies.
Albert Einstein provides a better quote on human behavior…
“If the facts don’t fit the theory, throw out the facts.”
Have you ever been in a leadership team meeting to fix a problem and seen this behavior? ( I know I have)
As many teams build and launch their sales growth plans this year, I wonder how many strategic programs were created based on data and how many were built with Bias.
A recent Mckinsey article By Giovanni Gavetti, Martin Huber, Dan Lovallo, and Magdalena Smith shared that we use analogies if we do not have data.
“Despite their best intentions, executives fall prey to cognitive and organizational biases that get in the way of good decision making.”
The authors share…
Business leaders often use analogies, but if an analogy is weak or similar to the issue at hand in only a superficial way, teams risk anchoring themselves to potentially ineffective solutions.
One form of bias is Cognitive Bias.
Cognitive bias is a mistake in reasoning, evaluating, or remembering. They often occur due to holding onto one’s preferences and beliefs regardless of contrary information. Biases are detrimental enough when they influence individuals. However, a cognitive bias in business is incredibly dangerous because it severely narrows the scope of perception of the decision-maker. This can lead to negative impacts, even in the most well-intentioned manager.
The good news is that there are a few steps to prevent bias affecting the decisions:
- Awareness that cognitive biases exist and how they can distort thinking. Be on the lookout for bias in yourself and your colleagues.
- Establish a consistent framework for decision-making based on data.
- Ask yourself if you have the correct information to make a good decision. Be armed with current data and reports rather than antidotes and narratives.
Case Study: Company A Attempts to Mitigate a Disappointing Q2 in Sales and Profit per Sale
It’s the middle of the second quarter, and Company A’s sales are flat. Typically, second-quarter revenues are the highest year-over-year.
The senior staff is worried.
They are concerned we have a possible canary in the coal mine for the rest of the year.
Is this a blip?
Poor execution?
Bad plan?
Or is it a sign that a recession is looming just around the corner?
The Senior Vice President (SVP) of Sales sees this as an opportunity to meet with his team and comb through their current and prospective accounts one by one to bring in additional revenue.
To aid their conversations, every rep must come to the meeting armed with four years’ worth of sales-by-customer, profitability-by-customer, and sales-by-vertical data and have a growth plan ready to present.
Meeting with his top-performing salesperson, a 20% discount is requested to tempt big accounts to buy more and move away from the competitor by the end of the quarter.
The VP learns that a small legacy account has issues requiring a lot of sales rep time to mitigate but little return.
Another rep thinks that a substantial prospective account will pull the trigger on their proposal if they throw in application services for free.
Another account wants last year’s pricing or won’t buy more products or renew their service agreement.
Another salesperson insists a new product is priced way too high, hurting the major vertical in his region.
What will the SVP do?
What have you done to fix sales problems with this kind of information?
After careful consideration, the SVP grants all pricing overrides and added value services and reports to the leadership team that he is confident the sales team will hit their number.
The second quarter ends, and the team eagerly awaits the final numbers—but there is terrible news.
The team has come in 17% under the target, and profit per sale dropped 2%.
Why?
Why did this happen
We had a plan!
The answer—is that, like many of us when we make decisions—the SVP has allowed his and his team’s biases to affect his judgment—which leads to an undesirable outcome.
This sales problem has elevated throughout the organization, resulting in more meetings.
The (SVP) of Sales and the CFO have called a joint meeting with various departments to discuss why sales are not growing to plan and even more concerning why net profits are falling below plan.
Sales operations chimes in…We should help salespeople create lists for organic growth targets in their regions and have sales spend more time prospecting. They should do region-specific reports that give them insights into where the growth plans are not working.
Marketing shares: we have seen this before, and back then, we held sales more accountable to follow up on all the leads we produce. We also want sales to stop creating their presentations and use our designed tools.
HR says it sounds like we have several skills gaps in both salespeople and sales managers, and it does not sound like sales managers are spending time coaching as we expected. We need to have the training to close the skills gaps in prospecting, key account management, and sales manager coaching.
The Pricing Manager says sales need to use the prices we provided and stop overriding, rebating, and discounting, and we will hit our targeted net profit numbers.
The CFO shares two slides.
Slide one shows a graph of our sales plan for this year and where we are to date. Sales are up but not growing at the rate we planned and more concerning it shows the profit per sale is declining.
The second slide predicts what year-end will look like if corrective action is not taken and sales and profits do not improve. We need our salespeople to stop selling on price and start selling based on the value we provide.
The Sales SVP shares how the sales team is meeting their key activity objectives in the CRM like number of ends customer visits, number of net new customer meetings, numbers of calls on current customers, and number of trainings at distributors. We need better sales tools. My team is working hard, and there are not enough hours in the day. I don’t want them going into BI and the CRM to create reports; I want them out selling, customer face time with better sales tools! I am beginning to question if the market prices marketing and product management provided are accurate given how often my team needs to provide our distributor’s rebates to win the business with end customers.
Meetings like this have taken place in many organizations over the years.
The good news is that you have a competent and experienced team committed to achieving the sales and profit goals you shared with the board and our investors.
They collaborate and work to solve problems and meet cross-functional goals. They don’t want to win; on the contrary, they are obsessed with winning and find themselves frustrated when the focused growth objectives are not met.
The trouble with the above conversations is that each person gives opinions based on their own experiences and biases and not actionable data.
Humans are Hardwired for Bias
Company A’s SVP has successfully led sales (for the last 15 years) to achieving year-over-year increases of 8%-12%. He uses the CRM reporting tool but trusts his gut for much of his judgment.
His gut has guided him well in the past. However, unless he occasionally goes against his intuition, it hasn’t been put to the test. There is no way for the SVP to know if his gut is helping him make good choices unless he occasionally ignores it to see what happens.
“The value of analytics projects often has much to do with the psychology of de-biasing decisions and the sociology of corporate culture change.”– Jim Guszcza, Bryan Richardson, Deloitte Review.
According to Noble Prize-Winning Behavioral Psychologist Daniel Kahneman, humans have two “systems” thinking.
System 1—where judgments are fast and automatic, stemming from associations stored in memory—and System 2, which is an effortful, controlled way of thinking that—once engaged, can filter System 1. It can be quite dangerous to rely solely on System 1 thinking because our intuitions, especially under stressful situations, often lead us to the wrong conclusions.
Everyone is susceptible to bias, especially if pressured or stressed. Reflect the SVP of Sales who is under pressure to “bring in the numbers.” He may be far from decision-ready in this situation, so he copes by relying even more on his gut and intuition—which in this case—means he doesn’t deliver on his number.
“Our comforting conviction that the world makes sense rests on a secure foundation: our almost unlimited ability to ignore our ignorance.”– Daniel Kahneman, Thinking Fast and Slow.
Kahneman theorizes that System 1 constructs a representation of a typical member of a population and then uses it to make judgments of other members. If facts contradict this interpretation, the brain finds it easier to ignore them. In other words, to our brain, the internal narrative we created about the typical member beats the actual statistics about the member almost every time.
Additionally, the human mind is biased toward stories with positive outcomes, regardless of their veracity.
If information is limited, the mind fills in missing pieces.
Humans tend to overestimate their prediction of events and discard the anxiety and uncertainty of not knowing. So, nearly everyone thinks too narrowly about outcomes. Some make one “best guess” and stop there. Others hedge their bets.
Unfortunately, our ability to predict the future is terrible at best.
When researchers at the Harvard Business Review asked hundreds of CFOs to forecast yearly returns for the S&P 500 over nine years, their 80% ranges were correct only 33% of the time. That is a meager accuracy rate for a group with vast knowledge of the economy.
Interestingly, projections are even further off when individuals access their plans because most of us tend to be overconfident—our desire to succeed skews our interpretation of the data.
Company A’s Overconfident decision making
Reflecting on the SVPs dilemma—sales are not at plan and profits per sale are down—and his various actions are not resolving the problems, it’s clear that he and his team are biased in their decision making and actions. Discounts are approved because, in the past, when they were offered, the deal often closed. This, however, probably isn’t the case. The sales team practices System 1 thinking because they each have at least one positive narrative of offering a discount and closing a sale. Kahneman would theorize that this narrative seduces the sales team with the “illusion of understanding,” meaning that the sales team’s recall of the time they-closed-a-big –deal when- they-discounted is inaccurate statistically.
Hindsight bias makes objective assessment almost impossible. Other factors not remembered or known, such as luck, timing, industry initiatives, and other external conditions, probably were more significant factors than the human mind can credit them for.
Have you experienced the symptoms of overconfident decision-making?
What impact did it have on your bottom line?
Using Data to Outsmart the Human Brain’s Bias
It was back in 1954 that psychologist Paul Meehl documented 20 studies comparing the predictions of human experts with those of simple statistical models. The studies ranged from how prisoners respond to parole to schizophrenic patients’ responses to electroshock therapy. The conclusion was that the human experts failed to outperform the models in 20 out of 20 cases. Meehl refers to this as his “practical conclusion.”
In this age of almost limitless computing power and big data, it is hard to overstate the importance of Meehl’s conclusion. Statistical analysis can augment better “expert” decisions in virtually every circumstance. Additionally, if we recollect Kahneman’s thinking systems, statistical analysis, and the resulting data can kick System 2 into gear—allowing slower, critical thinking—resulting in better decision making.
However, recalling our SVP’s sales problem, as he had both CRM data and BI reporting—how did he fail in his decision making?
In the context of his decision-making, the SVP was biased because of his thinking and the influence of the reps on his team and other managers in the organization
Moreover, while he had many reports, they only confirmed his bias because he could only track customer sales, profitability, and deals by vertical. So, in his mind, he was able to verify that price overrides and free services increase profits. He didn’t have a view into what each account was costing him and the net profit of each account. Hence, SVP had the “illusion of understanding” because he missed this valuable data.
The Net Profit by Customer Report: A Tool to Support Decision Making
Traditional accounting methods and reporting are often insufficient to drive prescriptive actions.
What is often not recognized or reported is which clients cost the most to serve and the least to serve. More concerning, even managers who understand the issue cannot easily distinguish between customers belonging to these two groups because they lack pricing analytic tools to determine. The total sales size does not always show that the customer is automatically the most profitable; sometimes, the most significant clients are the most unprofitable.
Luckily there is a reporting called net profit by customer.
The Net Profit by Customer often referred to as The Whale Curve, is a snapshot-in-time of cumulative client profitability, allowing organizations to capture the cost-of-sales and net profit by the client.
The Whale Curve depicts accounts where the sales team makes healthy profit margins, breaking even accounts and losing money. We plot accumulated net profit on the Y-axis, and on the X-axis, we plan clients from most to least profitable. The resulting curve looks like a whale coming out of the water.
In most cases, the most profitable customers create the most significant part of the organization’s profit. The Whale Curve makes this a prominent visual graphic. The Curves generally rise initially (these are the most profitable clients), then stabilize (break-even clients), and finally decline (clients where profit is lost.)
As a rule of thumb, the Whale Curve for cumulative profitability reveals that the most profitable 20% of customers generate between 150 – 300% of total profits.
The middle 60 – 70 % of customers break even, and the least profitable 10 – 20% of customers cause a decrease of 50 – 200% of total profits, leaving the company with 100% of total profits.
On the profitability Whale Curve, the difference between the highest point of the chart and current company profitability (100% profitability) represents unrealized profit potential for the company.
Thinking back to Company A’s decision-making, if the SVP had a Whale Curve report, which provided sufficient data, how might his decisions have been different?
- He and his team would have an immediate (possibly alarming) read where each of their current accounts fell profitability-wise after considering the cost of sale, making the price override decision significantly less antidotal (System 1). The data rendered would force the brain to kick over to System 2, engaging critical thinking.
- They may learn that the big client demanding a discount has been unprofitable for the last year.
- Additionally, the report would clarify no-charge services, increasing the accounts cost-to-serve and lower profitability.
- Finally, if he ran a Whale Curve on products and verticals, he could determine that the new product isn’t priced too high, it’s already profitable, and the vertical pricing in question is at-market.
Next Steps After Determining Profitability with the Whale Curve
The results of the Whale Curve often prove to be surprising, even more so when biased inclinations are proven incorrect. Using the Whale Curve, the SVP should have:
- Given the sales reps the Whale Curve report. Discussed the profit-losing accounts and strategized solutions. This data must be given to the individual who deals with clients.
- Found services that cost the company money, but the client does not value and remove them. Possibly change the terms of trade and move targeted accounts into a self-service model.
- Sell profit-leaking accounts additional products or services. His blended margin could make the client profitable.
- Held a brainstorming meeting with the entire sales teams to discuss territory Whale Curves and brainstorm possible profit-leak solutions.
- Engaged with a third party to deliver additional data and analytics.
- Complete a mid-year gut check
Actionable Information and Self Awareness Arm the User Against Bias
Ultimately there is no cure-all data set or reporting that prevents decision-makers from bias. Humans are naturally wired to seek out data and information that preserves their beliefs and decisions. Ahneman suggests we challenge our own decision-making bias by using these three questions:
- Is there any reason to suspect the people making the recommendation of bias based on self-interest, overconfidence, or attachment to past experiences?
- Have those making the recommendation fallen in love with it?
- Was there group think or were there dissenting opinions within the team?
In addition to posing these questions, organizations should constantly seek more data and consider using reporting like the Whale Curve that upends the typical, status quo sales analytics and depicts a new, actionable perspective.
For more information about bias mitigation, making strategic decisions based on data analytics, and the Whale Curve, don’t hesitate to contact me at markrobertsnosmoke@gmail.com .
