June 9, 2021

Written by

Collective[i]

  • Posted in
  • Sales Forecasting

What are the top 10 difficulties of sales forecasting

If businesses could easily and accurately predict just how much money they would bring in over a year, the world would look a lot different. There would be less fluctuation in the economy, less job instability, and probably no need for articles like these.

Unfortunately, that’s not the case. Sales forecasting is difficult, no matter how good and dependable the sales team producing it. Yet it’s also one of the most important aspects of running a business. Whether preparing for the coming year by hiring and training staff in advance (or, unfortunately, trimming them), or even securing capital for investment and expansion, businesses need some idea of what to expect.

Many factors make sales forecasting difficult and, in some cases, unnecessarily complicated. There are also many external forces that create difficulties in sales forecasting, and often these are beyond the organization’s control.

Fortunately, many businesses operate the same way. That means they face the same challenges, including in sales forecasting. Let’s look at the top 10 causes of bad forecasting — and how they can be avoided with the right tools.

#1: Inaccurate CRM data

An overwhelming majority of companies use some sort of CRM to track potential sales and customers. Unfortunately, the task of keeping it updated isn’t always honored. Many salespeople may view it as “paperwork,” and why shouldn’t they? It’s simply work they have to do after having a meeting. That kind of busywork can be difficult to prioritize when sellers are working hard to reach their numbers, not to mention the opportunities for mistakes with manual data entry. Through neglect or accident, the result of bad CRM data is the same: bad forecasts.

#2: A lack of insights

For those instances where sellers do have up-to-date data in their CRM to build forecasts with, the problem sometimes lies in the analysis of it. No matter the CRM a business chooses to use, if it doesn’t actually crunch the numbers to make actionable projections, those projections aren’t worth much. This is a limitation of both the tool and the user; many sales forecasting tools can create a projection, but won’t tell sellers what they need to do to actually reach that goal. Likewise, sellers might know what their target is, but struggle around which deals to prioritize to make their number.

#3: Incomplete data

A sales team’s CRM isn’t the only source of data that could be used to inform sales forecasts. The best maintained CRM database still can’t account for other factors that could impact buyer behavior, like broad market movements or changes in individual buyer purchasing patterns. For example, a seller might think a deal is close to closing because it’s in the last opportunity stage in her funnel, but what if there has been a buying freeze at a prospect’s company that even that prospect doesn’t understand? Without the ability to see how buyers are interacting not just with her sales team, but also with other sellers, this seller is operating with incomplete information.

#4: Ineffective tool implementation

Sales teams love their technology, and there are plenty of tools on the market designed to help a team track and convert sales. In fact, many organizations use more than one tool, and some upwards of 10. The problem isn’t always in the tools themselves, but often in their implementation. The more work individual sellers have to do to keep track of their data and provide reports, the less time they actually spend selling. Which is why sellers often fail to harness the full capability — and subsequent benefits — of selling tools.

#5: Over-reliance on subjective data

In any given sales meeting, a seller could report to their manager that a particular deal is going well. But what does that actually mean when it comes to predicting the probability that deal will actually close? Effective salespeople are confident; it’s one of the traits that make them good at what they do. But it’s not always possible for sellers to know everything about a moving outcome, and their gut feelings can be entirely off the mark. Modern sales teams know that the key to a usable sales forecast is the proper balance of instinct and information, so it’s not too much of a surprise when a “sure thing” fizzles out.

#6: Fighting consequences

Sales teams are often faced with unreachable goals as a result of poor forecasting. In those cases, it can be particularly difficult to admit a relationship has turned south — especially if sellers think their job is on the line. Even when presented with solid data and realistic forecasting, headstrong leadership may want to inflate those numbers — which puts even more strain and fear on the sales team instead of empowering them to drive revenue.

#7: Sandbagging

When leaders put too much focus on sales forecasts as targets to hit, it can incentivize sellers to manipulate their own data. Especially when a CRM isn’t kept up to date, it’s easy for a salesperson to push a likely sale to the next quarter to keep their own numbers consistent. That, too, makes it difficult to accurately predict sales patterns, as the business can’t be sure what behaviors and patterns to expect from customers.

#8: Unrealistic timelines

It’s difficult to accurately predict exactly when a client will finally sign that deal. The blame doesn’t fall on any one person in particular — maybe there are limits to what a sales team’s chosen forecasting tool can do, or new customers will behave differently from existing ones. The more new buyers are expected to behave the way others have in the past, the more likely it is that projected timelines won’t pan out.

#9: Inconsistent methodologies

Science is reliable because it’s repeatable; under the same circumstances, a similar (if not the same) result occurs. Sales teams need to analyze data under consistent methodologies — that is, the same process — in order to achieve accurate results. In many cases, each sales manager is allowed to develop his or her own methodology for forecasting. This makes it all but impossible to compare forecasts. Worse yet, when they’re compiled into larger corporate forecasts, many of these errors get amplified, so the mistakes and miscues read as even greater than they are.

#10: Limited technology

CRMs exist to track interactions with buyers and manage leads. However, they rely so heavily on seller input and interaction that they break down quickly if data isn’t actively supplied and nurtured. Because many companies rely on manual data entry or old school methods based on intuition, well…the breakdown becomes pretty evident.

So with all of the causes of bad sales forecasting, what’s the solution? Technology that automates data entry while harnessing the power of artificial intelligence: Collective[i]’s sales forecasting platform. It automatically records every interaction between potential buyers and selling teams (including non-selling employees like the marketing, legal, and procurement teams). It studies individual buyer activity from your company’s interactions as well as a growing network of buyer data to actually learn the way individual customers buy and help sellers think customer-first. And with daily updates, it gives sellers the power to adjust their sales forecasts in real time, so leaders can make better, more thoughtful decisions — and ultimately win more deals.

Try it out here.

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