Guide
Predictive Analytics: A Business Buyer's Guide
Predictive analytics uses your historical data to forecast what is likely to happen next. That might be demand, failures or customers leaving. You can then decide with foresight rather than hindsight. This guide is for the person deciding whether to buy it. It covers what predictive analytics can do, what data it needs, how to evaluate a supplier or a platform, and what it realistically costs. It also covers the most common outcome of all: a model that works technically and gets used by nobody.
Key takeaways
Buy the decision, not the model
If a forecast would not change what someone does, it is not worth paying for.
Your data almost certainly matters more than the algorithm
Ask any supplier to assess it honestly before you sign anything.
Accuracy is not the goal
A slightly less accurate forecast that reaches the decision maker in time beats a better one that arrives too late.
The most common failure is not a bad model
It is a good model that lands in a dashboard nobody opens.
What predictive analytics can genuinely do
Predictive analytics works by finding patterns in what has already happened and projecting them forward. That sounds modest, and it is. There is nothing mystical about it. But applied to the right decision, it is valuable. Most businesses make forward looking decisions using backward looking reports and a lot of gut feel.
The uses that reliably repay the investment share a shape. They involve a decision made repeatedly. Being roughly right in advance is worth more than being exactly right afterwards. And enough history exists for the pattern to be real rather than imagined.
Demand forecasting
How much you will need, and when. This is usually the highest return use, because getting demand wrong costs twice: in lost sales and in tied up capital.
Predictive maintenance
Which equipment is likely to fail before it does. Unplanned downtime becomes a scheduled task.
Churn prediction
Which customers are likely to leave, found while there is still time to do something about it.
Revenue and cash forecasting
What is likely to come in and when, based on real patterns rather than a spreadsheet extrapolation.
Risk signals
Problems flagged while they are still cheap to fix, rather than found in a post mortem.
Capacity and staffing
Planning people and resources around what is coming, rather than what happened this time last year.
What it cannot do, honestly
Predictive analytics projects patterns forward. That means it is poor at predicting things that have no precedent in your data. A model trained on three years of steady trading will not see a shock it has never met. Any supplier implying otherwise is overselling.
It also cannot fix a decision nobody has the power to make. Say your team already knows demand will spike and still cannot act. Approvals take three weeks, or the supplier needs six weeks' notice. A better forecast changes nothing. That is a process problem dressed up as a data problem. It is worth spotting before you commission a model rather than after.
And it cannot create a signal that is not in the data. Suppose the thing you want to predict depends on factors you have never recorded. No algorithm recovers that. A good supplier will tell you this at the scoping stage. A poor one will build you something anyway and let the accuracy disappoint you later.
What data you actually need
The honest headline: your data matters more than the algorithm. Modern modelling techniques are widely available and largely commoditised. What separates a forecast that works from one that does not is almost always the quality, consistency and relevance of the history behind it.
You generally need enough history to contain the pattern you care about. For anything seasonal, that means multiple cycles. One year of sales data cannot tell a model with any confidence what December looks like next to July. You need the data recorded reasonably consistently, because a change in how you categorised products two years ago can quietly break a model. And it must contain the thing being predicted. To forecast equipment failures, you need a record of past failures, as well as maintenance visits.
You do not need perfection. Working with real, messy business data is the job, not an obstacle to it. Any supplier who requires pristine data before starting either has not worked with real businesses or is managing their own risk at your expense.
How to evaluate a supplier or platform
Broadly there are three routes. The right one depends far more on your situation than on which is objectively best.
| Factor | Off the shelf platform | Commissioned build | In house team |
|---|---|---|---|
| Best when | Your need is common and well defined: standard demand forecasting, standard churn scoring | Your decision is specific to how your business works, or the data is awkward | Predictive work will be continuous and central, not a one off |
| Speed | Fastest, often weeks | Moderate, scoped per project | Slowest by far from a standing start |
| Fit to your process | You adapt to the tool | Built around your decision | Fully tailored, over time |
| Cost shape | Ongoing subscription | Fixed project cost plus running cost | Salaries, plus a long ramp before value |
| Main risk | Generic forecast that does not fit your reality | Choosing a supplier who builds the model but not the adoption | Hiring specialists with nobody experienced to direct them |
Most businesses buying predictive analytics for the first time are better served by a platform or a commissioned build than by hiring. A single data scientist without experienced direction is an expensive way to find out what you needed.
Questions to ask before you sign
These questions separate suppliers who will deliver value from suppliers who will deliver a model.
What it costs, and where budgets go wrong
Cost varies enormously with scope. Anyone quoting a figure before understanding your decision and your data is guessing. What is consistent is the shape. A focused project forecasting a single decision is a contained, scopeable piece of work. A broader system feeding several decisions across an operation is a bigger project, priced accordingly.
Two things reliably blow budgets. The first is scope creep from starting too broad. "Let's forecast everything" has no natural end point. "Let's forecast demand for these forty product lines" does. The second is the ongoing running cost, which is easy to overlook at purchase. Models need maintaining and data pipelines need running. Where AI models are involved, they charge per use. Ask for that figure before you commit, not after.
Why most predictive projects quietly fail
They do not usually fail loudly. There is no moment where the model breaks and everyone notices. Instead the project completes and the model is technically sound. The output goes into a dashboard, and then nothing changes. Six months later nobody is opening it. Decisions are still made on gut feel. The conclusion drawn is that predictive analytics did not work for this business.
The cause is almost always the same. The project started with the data rather than the decision. Nobody established, before building, which decision the forecast was meant to improve. Nobody asked who makes it, when, and what would have to reach them to change what they do. Without that, the model has nowhere to land.
The most useful thing you can do as a buyer is refuse to commission anything until that question has a specific answer. "It will improve our planning" is not an answer. "It will tell Sarah, every Monday morning, how much of each line to order, in the system she already uses" is. That level of detail is the difference between a model that changes the business and one that decorates it.
Buyer's questions about predictive analytics
What is predictive analytics, in business terms?
Predictive analytics uses your historical data to forecast what is likely to happen next. Examples are future demand, likely equipment failures, or customers likely to leave. Standard reporting describes what already happened. Predictive analytics aims at foresight: seeing what is coming in time to act on it.
How do we know if our data is good enough?
You need enough history to contain the pattern you care about, recorded reasonably consistently. It must include the thing you want to predict. For anything seasonal that means multiple cycles. It does not need to be perfect. Any credible supplier will assess this honestly before you commit, and tell you if the answer is "not yet".
Should we buy a platform, commission a build, or hire in house?
A platform suits common, well defined needs and is fastest. A commissioned build suits decisions specific to how your business works. Hiring in house makes sense once predictive work will be continuous and central. Most first time buyers are better served by the first two. A lone data scientist without experienced direction is a costly way to learn what you needed.
How accurate should we expect a forecast to be?
Be wary of anyone quoting a confident accuracy figure before seeing your data. Raw accuracy matters less than two other tests. Is the forecast good enough to beat your current way of deciding? Does it arrive in time to be acted on? A slightly less accurate forecast delivered at the right moment beats a better one delivered too late.
What is the difference between predictive analytics and decision intelligence?
Predictive analytics produces the forecast. Decision intelligence turns that forecast into a specific recommendation, delivered where and when the decision is made. The forecast is the input. The recommendation is where the value is realised.
What is the biggest risk in buying predictive analytics?
Commissioning a model before establishing which decision it is meant to change. That one omission causes most quiet failures. The model works and the output goes into a dashboard, but nothing about how the business operates changes. Insist on a specific answer, naming the decision, the person and the moment, before signing anything.
Work out whether it is worth buying at all
Book a free consultation. Tell us the decision you would want a forecast to improve. We will tell you honestly whether your data can support it, what it would take, and whether it is worth doing, before you spend anything.
If you decide to build, see AI software development. Factories can read AI for manufacturing.
Check whether it is worth buying
The first call is free. We tell you whether predictive analytics would pay off for your data.