We already discussed how important sales forecasting is to any business, not matter B2C or B2B. Proper forecasting is an efficiency booster since it is enabling proper recourses planning like staff, stock, financials, logistics, production, etc. This is especially important for businesses producing limited shelf life products.
One single KPI makes the sales forecasting good or bad. It is the accuracy. The accuracy is the crucial indicator paving any business plan to the road of success or failure. In this blog post, our data analytics team at A4E is sharing tips and hints on how to boost your sales forecasting accuracy.
Forecast by acquisition channel
Sure, it depends on the business but if you are forecasting sales of actual products/SKUs you need to consider the acquisition channels and their conversion rates. This is way more granular approach and in the end of the period, it might give a more accurate picture. For instance, if you are bathroom retailer with an online shop, you have to forecast the website traffic you are going to attract divided on the different acquisition channels like social, organic search, referrals, etc. Then is the forecasting of the foot traffic into existing retail stores. On all of this, you have to apply the conversion rates your potential customers’ traffic is generating. On top of this, we might add an advertising campaign, which will change both the traffic and conversions. The campaign will be more or less successful depending on the media mix, the copy and the graphic production, duration as well as the focused product group.
Review sales forecasting at the right time
Before you finish the forecasted time frame we have to look at the mid-period performance and to examine the generated pace. Such evaluations are stating if the estimations are overconfident or right the opposite or they are correct. Such evaluations enable the definition of the deviation factors if any. Such factors should be added to the variables that will be taken into account when new forecasts are made. In addition, the sales forecasting review and follow up corrections is not just giving knowledge on sales triggers but also boost its accuracy.
Forecast sales with data analytics tools & solutions
We all know MS Excel masters able to forecast sales in a minute. What they can’t do is to account specific variables in a proper way and last but not least – to rate the forecast` accuracy. It can be done with self-learning data analytics algorithms identifying correlations and impactful variables. They are way more advanced than the simple statistical approach and the Exponential Smoothing algorithm utilized in MS Excel. The tool for retail sales forecasting developed by A4E is giving its users specific data column with Forecast Accuracy for every particular SKU. It gives an insight on the overall SKU sales forecast quality and experts says that anything above the 85% mark is more than good. Just take a look at our a4RetailStores sample report, select a particular product and browse the forecast. Take a note it gives its users’ sales forecast accuracy as of 94.7% for the top selling products, forming 20% of the total sales.
Learn from the past
We`ve already mentioned few times how important variables are. Factors with sales impact are the key to the forecast accuracy. You can find them anywhere including where you do not expect. Better performing sales person, seasonality, a sudden event, trends, advertising, competition, just name it. Review any significant peaks or downs in the past sales and answer the question “why it happened”. This is a shortcut to the identification of affecting variables. Data analytics solutions are capable of automated variables identification and their proper extrapolation in the future in order to achieve even more accurate sales forecast. 
