Note: KPIs, analytics, predictive analytics — we tend to use these terms interchangeably, but they’re not. They overlap, but progressively more skill is needed as you move along the analytics continuum. If you are thinking of a first analytics project, the best advice is to keep it simple and manageable. Once you have some experience and you’re ready to step up choose something small, yet likely to have an impact. The following article will help you decide what problem to tackle that will make a difference — beyond just HR.
1. Identify a business problem to solve.
Almost everyone working on modern predictive analytics discusses the need for a defined business problem before engaging in a predictive project. And yet, the #1 question I get in speaking with businesses is, “I need to do a predictive project, but I don’t know what to work on.”
Without a specific problem to solve, your analyst or vendor will do nothing more than crunch data hoping to find something interesting. Crunching data without a specific objective, is a very expensive and typically a very unproductive use of your company’s time and money.
Consider these examples:
- Imagine searching for a house on the internet before you’ve decided what kind of house you want. You’ll find some interesting ideas but nothing that will make you act on buying the house.
- Imagine reading Wikipedia looking for some “truth” without defining what question you’re trying to have answered. You’ll find some interesting ideas but likely none of them will give you the truth you’re looking for.
You need to first know what you’re looking for before you embark on a predictive project.
2. Decide if you want to solve an employee-related process problem in HR, or an employee related business problem in a line of business.
Examples of HR problems: Predict who is going to retire; predict which training will yield the highest attendance; predict how your current hiring processes will affect discriminatory hiring practices; predict your company’s requirements for engineers
- Benefits of solving an HR problem: You’re solving a problem that is meaningful to your department (and typically only to your department).
- Downside of solving an HR problem:The rest of the business will be less excited about the work you’re doing to solve an HR process problem.
It is harder to quantify the business impact of solving an HR process problem.
Examples of workforce problems: Predict which job candidates will be top sales producers for open sales roles; predict which call center reps will be a good fit as a call center manager; predict which truck drivers will be in more accidents.
- Benefits of solving a workforce problem: You’ll solve a problem that is meaningful to the entire business as it is likely to affect revenue or cost. This will get your project much more visibility and additional resources for ongoing predictive work.
- Downside of solving a workforce problem: You are likely to get many more requests for predictive work from the business after they see the kind of work you can do that affects revenue and cost.
3. Combine HR data with line of business data.
If you are looking to predict and solve a workforce problem in the line of business (e.g. increase sales, reduce errors, increase calls per day and the like) the data in the line of business exists in software systems in the lines of business, not in HR. For example, sales performance or calls per day data exists in sales operations or the call center or some other non-HR database somewhere.
You can’t predict which sales candidates are going to make their sales numbers without sales data from the sales department. You need to use line of business data as well as HR data. Unless you only want to predict something that impacts HR, you’ll need data from the line of business as well.
4. Go beyond predicting trends. “Individual” predictions deliver the greatest ROI.
Many departments have been forecasting trends for years – and in fact, many predictive projects we hear about are in fact older-school forecasting projects. We need to move beyond forecasting to deliver the kinds of ROI that excites your C-suite.
Forecasting examples include:
- Forecasting future inflation rates.
- Forecasting product demand.
- Forecasting workforce trends over the next 1 – 5 years so you can plan.
- Forecasting sales next quarter, next year.
While forecasting is extremely necessary – it is quite different than modern predictive analytics initiatives. To reap the ROI of modern predictive capabilities, organizations need to move to predicting to the individual.
Predicting “to an individual” examples include:
- Predicting which specific job candidate has a high probability of being a top or bottom performer.
- Predicting which specific customer prospect is going to click the coupon and buy the offer.
- Predicting which specific vendor is going to go out of business.
The ability to predict to this level of granularity should be the goal of modern predictive projects. ROI is higher because it helps your company to take specific action with high cost or high revenue potential targets.
5. Go beyond predicting flight risk of existing employees. Make a prediction about flight risk before you hire a candidate.
Many companies focus on predicting the flight risk of existing employees as an early predictive project. This reminds me of a bank predicting which loans will fail after they’ve already loaned money. After the relationship is extended is the wrong time. It’s too late.
Modern predictive analytics allows you to predict “before.” That’s the point. Predict before you make the mistake. Banks put a lot of effort into creating predictive models that predict your probability of paying or defaulting on a loan before they extend the loan.