It depends on the character and quality of the available data. Ideally, that data will include at least one binary dependent variable and several potential drivers, as in our simulations.

We think of percentage point effects simulation as a quasi-experiment. An implication from a design perspective is that you should conceptualize your survey or information system as a platform for estimating causal effects. The platform should include the variables needed to enable percentage point effects simulation. In principle, the dependent variable—the one you’re trying to grow or shrink—should be a true dichotomy from the start (rather than, say, a 10-point scale you transform to a dichotomy post hoc). And the predictor variables, aside from any socio-demographic ones, should be potential levers.

If you're trying to figure out what question types to include as predictor variables in a survey, follow best practice. Recently conducted research suggests that unipolar, four-category, fully-anchored scales work well (in terms of usability, reliability, and validity) across modes (e.g., mobile, online, telephone, face-to-face, mail) and languages. Think about using two-point scales, too—they ease interpretation. You also should keep in mind the concept of linking and syncing. It involves enhancing the extent to which the data sources on which you rely share common factors (e.g., sampling frames, data collection dates, questions). (See our data linkage FAQ.)

Data linkage is the act of putting together different data sources to enhance the usefulness of the combined information. It also can involve using data from one source to adjust data from another—we call that a link and sync process. For instance, a brand might use key data from market size and share reporting as a check and basis for adjusting survey information (e.g., self-reports of purchase behavior) from a brand health or customer experience survey. It would be akin to how Pew, Gallup, Ipsos, or YouGov use Census data (e.g., population percentages for key demographics) to adjust survey data. To describe that process, they would say something like this: results were weighted for age within sex, region, and race-ethnicity…to align them with their population proportions. Although data linkage may seem sensible in theory, it can be difficult to apply, particularly when the targeted data sources were neither designed nor conceived of as neatly-fitting puzzle pieces.

Those systems lack built-in modules for producing and reporting percentage point effects so custom programming would be needed.

After you assess all plausible scenarios, develop a plan to increase the target populations's probability of belonging to the group of interest (e.g., customers). A successfully-executed plan will increase the size (and, where applicable, spend) of the key group. You'll also need to think through how difficult it may be to make a particular change (or changes).

Our simulator produces predictions with 95% confidence intervals though some clients prefer that we not show them. With that said, those intervals can be an important safety check. In general, predictions based on larger sample sizes are more trustworthy than those based on smaller ones. By reporting 95% confidence intervals, or lower and upper estimates, we quantify the trustworthiness of our predictions. Here's how you should interpret 95% confidence intervals: "If the study had fielded (or had the same data been collected), say, 100 times, and nothing else had changed, then our predictions would lie within the upper and lower estimates 95 times out of 100."

That statement assumes that there were no biases other than those associated with how cases (e.g., people) were selected (i.e., sampled). That may not be realistic. In survey research, potential biases include non-coverage error, non-response error, question wording, and question order. Other data types (e.g., point-of-sale) may suffer from different biases.

In logistic regression analysis, those terms are synonyms for a variable (i.e., “an attribute that describes a person, place, thing, or idea") on the right-hand side of the equation. They explain or predict the binary dependent variable, or y. Sometimes, they can be thought of as levers, actions, linchpins, or drivers (e.g., a mobile check-in option at a hotel)—it depends partly on what they describe (e.g., a person, place, thing, or idea).

The model is unlikely to change meaningfully from month to month. Our suggestion, absent additional information, is to update it once or twice a year. It will give you time to implement the actions you've identfied to affect the key outcome(s) from the tracker.

Electric Insights is a new start-up so we don't have many clients, yet. With that said, we've worked with data sets covering several broad areas, including patient outcomes, sports performance, hospitality, public relations, public opinion, and sales performance.

We'd start by reviewing your objectives and the data available to support them. If you have what you need, we'd work with you to identify the group(s) (e.g., customers) of interest. We'd then build one or more logistic regression model to understand which predictor variables move the needle. Once we decide on the ultimate model(s), we'd generate all predicted probabilities before building the simulator(s). We'd also make sure you understand how to use it. If you don't have the data you need, we'd advise you on how to produce it. Topics we typically touch on include research design, data linkage, and any other that contributes to better data, reporting, analysis, and decisions.

If the future is like the past, our predictions will be accurate.