Fine tuning your sales forecasting for a more transparent future

Every company on earth, of whatever size, wants to be able to see the future. A sales forecast is the estimated sales a company can expect to make within any chosen forecast period, be that a week or a year. Accurate forecasting of future sales enables optimised planning and decision-making in every element of a business, from buying and hiring to expansion and advertising.

As important as it is, however, forecasting methods can be precarious. So many factors that can impact sales are unforeseeable, out of a company’s control. But when projected sales are off kilter, everything else is, too. So which methods give companies the most reliable sales forecasting? And can exciting technology like artificial intelligence (AI) help humans make better forecasts for better business?

The importance of sales forecasting

Sales are the engine of any business, be it sales of services or products. Profits from sales enable the purchase of replacement stock to sell, and the payment of employees and other essential operating expenses. Healthy sales attract great word-of-mouth, and yet more purchasers and investors, funding new and improved products and services, and giving marketing campaigns a longer reach.

Great forecasts can help you to:

  • Spot potential issues and head them off before they do damage
  • Better manage both your people and your portfolio
  • Plan ahead for and target sales and marketing strategies and campaigns
  • Improve individual, team, and overall performance: a forecast is also a goal

It goes without saying that low or wildly unpredictable sales affect all those and more. Without insights and performance prediction from informed sales forecasting, then, how can you carry out the most effective financial, marketing, and other planning for optimal Return On Investment? You can’t. So what do you need to watch out for?

Factors impacting Sales Forecasting


Knowledge as to which factors affect sales forecasting within a company is invaluable, as they can be controlled, and negative impacts mitigated or prevented. It can be used to bring about positive impacts, including improved forecasts and sales.


While a company may have little to no control over the prevalence or virulence of these factors, awareness of them allows for improved forecasting and other planning.


Main sales forecasting methods

Whether you’re seeking sales forecast data for the purposes of simple short-term budgeting, seeking outside investment, or taking a company public, choosing the correct method is the first thing you need to get right.

Qualitative Forecasting Methods


Quantitative Forecasting Methods


The AI advantage in eCommerce sales prediction

There is probably no other element of the journey to business success so demanding of time or variable in outcome than sales forecasting. With so many elements of data within each prediction being a mere estimate, the end result is often little more than guesswork.

Worst of all, inaccurate forecasts tend to err on the side of over-optimism, so targets are less likely to be met, giving companies and their employees a depressing sense of falling short which may not even be justified. Given its importance, as outlined above, this is obviously an eCommerce problem in serious need of a serious solution. Regardless of a company’s size or scope, it can hit quotas and targets and increase revenue year-on-year by putting AI to work within its teams.

  • You’ve seen how pipeline analysis provides data for forecasting. An important part of this is lead-scoring, and humans tend to warp scoring predictions with optimism and faulty intuition. Specialised AI software doesn’t have such human characteristics to interfere with its analyses of scoring probabilities.
  • Relatedly, a business succeeds by its repeat customers. Preventing customer churn is one thing, growing their loyalty another. Even if a customer’s needs have been met by a company in the past, those needs may change, and a business that fails to note this may lose them. It costs far more to attract a new customer than retain a loyal one. Humans and AI working together can spot which customers feel less well-served before that happens.
  • Predictive analytics now possible with AI can not only pinpoint which customers are in need of what attention to keep in and guide them through the pipeline, but also which employees and internal actions best influence this. The insights provided by such data make forecasting more accurate and improve performance at many levels as a bonus.
  • Because AI analyses patterns of demand, eCommerce sales, marketing, and portfolio experts get ongoing insights which support them to plan everything from ordering and investment to campaigns and funnel health. Special offers can be made, for example, bundling items with slowed inertia so as to sell more while giving potentially churning customers a bargain that retains them.
  • The old ways of forecasting took a great deal of time but could only lead to guesstimates in the end anyway. All of the benefits of modern AI outlined above give a double gift: providing more accurate, fast, and data-backed forecasts. And so letting all of a company’s experts focus on other things, such as making the best actions and decisions based on the insights received, and constantly improving their customers’ journeys.

An AI-powered predictive tool works alongside existing digital resources, maximising the opportunities offered by the data they have captured and will continue to gather for continually updated insights. This sounds very specialist, but the latest software is designed for ease of use, and friendliness with apps people already use, so that anyone can use it like a data scientist without having to be one, obtaining swift and accurate answers to relevant data-based questions.

For example, you can ask your AI:

How does a particular item relate to wider sales trends?

What quantity of each specific product will be sold within a specific forthcoming timeframe, perhaps the coming days or weeks?

Which unique portfolio item or items are slowing down or gaining momentum?

Being able to access accurate profiles through these predictions through different layers of metrics – single transactions; frequency, quantity, and volume of total sales; and broader information on seasonality, trends, and volatility – males rolling forecasting a snap, the insights gained informing every element and action of a business from budgeting to marketing.

The forecast is fine if you choose the appropiate forecasting method

According to Salesforce, sales reps spend only 34% of their working hours actually selling. Much of the remaining time is taken up with work that machine analytics software could be getting on with, enabling reps to focus on meeting quotas and satisfying customers. This scenario is a win for forecasting and success: Salesforce finds underperforming sales teams are 1.7 times more likely to base forecasts on intuition, while high-performing teams are 1.5 times more likely to wield insights driven by hard data. A reliable shared data pool also helps sales professionals attain their desire to work well on a cross-team, company wide basis, all on the same page.

Accurate, interpretable, and shareable, Shimoku’s Demand Planning Suite provide sales forecasting on a granular level for each individual product. The AI can tell you how much you’ll sell in near future, and whether a product is trending, waning, or accelerating. It can tell you which customers are more likely to churn or return. All of this data means not only increased revenues and improved journeys for customers and employees alike, but provides a wealth of data from which insights emerge to further improve your forecasting methods and reliability.