We're looking for help developing Forecast. If you have specific business needs, or specific forecasts or reports you'd like to see developed, please get in touch
Put simply, Forecast is a new reporting module to create accurate forecast metrics for turnover and rebate.
Would you like to know how much the group is likely to spend on Power Tools this year? Would you like to know how rebates will be affected if your largest member leaves next month? Would you like to know the effect a new member joining in Scotland will have on overall purchasing power?
These are all question we're trying to answer by developing Forecast.
Before we dive into what Forecast actually does (or will do), first, let's backup and talk about forecasting in general. Companies today use everything from simple spreadsheets to complex financial planning software to attempt to accurately forecast future business outcomes. These tools build forecasts by looking at a historical series of data, which is called time series data. For example, such tools may try to predict the future purchases of a member by looking at their previous purchase data with the underlying assumption that the future is determined by the past. This approach will generally fail to produce accurate forecasts because data has irregular trends and shifting variables - both internal and external. Even when forecasting uses seasonality models, it's still looking only at historical purchase trends, and not taking into account other data series that change over time (such as category trends, member branch size) with relevant independent variables like store locations or the general state of the economy at the time.
Using Machine learning and AI, when you provide data to Forecast it will examine it, identify what is meaningful, and produce a forecasting model capable of making predictions that are up to twice as accurate than looking at time series data alone.
If you forecast only using your own internal data, regardless of how verbose it is, you will not be able to factor in external data which can affect your historic data and therefore your forecast.
Forecast automatically imports publicly available data which compliments your internal data. For example, Forecast automatically includes the following time related data:
All of these things can influence your historic data in ways you cannot understand without spending weeks or months analyzing the data at a micro and macro level. Using advanced AI, Forecast finds relationships between these variables which it uses and factors into its forecasts.
Now, you may be thinking "that's great, but a wet July affects my sales, not my purchases!". It doesn't matter; if these are lagging indicators or if they have no bearing on your purchases or inventory levels, Forecast work that out and weight your data accordingly, automatically.
Le's say it's August, and you want to forecast group spend to the end of the year. With completely static variables, forecasting with simple seasonality data is generally adequate. However, the world is not static, and neither is your business.
So, let's add some variables. Let's say in the last few months, two of your members have changed.
Ok, on the face of it you've got five new branches, and have lost five branches - so it would be tempting to see these i reductionist form, but let's add some further variables we know about to demonstrate how unwise that would be:
Let's add some further external variables to really muddy the water:
You can hopefully see that simply looking at last years spend and thinking it is going to any better than throwing spaghetti at the wall is a fools errand. Furthermore, if you include last years spend in a seasonality seasonality template that wouldn't help your forecast either.
eBiz Forecast can factor in all these variables using ;Artificial Intelligence developed over 10 years and used in the retail business at Amazon.com to analyse, and find patterns in your data by using deep learning algorithms to provide predictions that can be trusted, back-tested, and that are considerably more accurate than using seasonality and time series data alone.
We're still working on that. Training vast sets of data using AI is currently very expensive. However, we plan to work on getting the costs down, so at present we estimate that we can deliver a free tier (for example, 10-20 forecasts for free so you can evaluate), but after that there will be a fixed cost to use Forecast.
We're developing Forecast now, and we expect general availability to be Q2 2020.
We're excited to start building Forecast, but we'd love your input so we can make it as useful as possible. If you'd like to contribute, please get in touch so we ca discuss your ideas in more detail.