Your organization has a plethora of data: website activity, demographics, app, backend, store, and more. Do you ask yourself and your team what key attributes drive desired behavior? What data is supercritical to understanding customer responses? Or what data is inconsequential and won’t help you land more sales?
Do you want to anticipate your prospects’ and customers’ next move? Can you be there when they are still thinking? It pays to be proactive! Being an early consideration vendor increases your chances of an action or a sale 15x or more vs. latecomers.
Control the future with Predictive Analytics and Data Science without relying on teams of analysts and managers.
How many times have you had high hopes for experiments that you are running only to find no lift? How many months and weeks have been lost? What was the cost of the lost opportunity?
If your analysts do not have experience building the models you need, it may take them a while to figure out which modeling tools and techniques to use in specific situations. And if they do not practice advanced data science, they still may need training on the latest or niche tools outside of their specialization.
We fill in the experience gap of your team and speed up collective learning and time-to-results.
The accuracy of modeling greatly varies whether you work with homegrown models or the models built by experts. Besides using the right model, the right data, and the right tools, the expertise required goes beyond data science.
We account for macro factors impacting your business, the industry, and the economy as a whole for more precise predictions.
Future is not always a straight line stemming from past events. Some future actions have no past: anticipated behavior has no past references. Building the models without history requires the most advanced data science skills and experience, rarely available in-house.
We help you meet the challenges of the future regardless of history.
e-CENS offers end-to-end predictive modeling services for customer acquisition, retention, and any other key function of your organization. The full-cycle project includes the following stages:
We spend a considerable amount of time defining and refining modeling goals to ensure that the model will deliver the answers and meet your expectations. In complex organizations, multiple teams and stakeholders are involved—our experience working in this complexity results in a more widely used and acceptable deliverable.
Data is quite raw in nature and needs significant hands-on structuring, cleaning, normalization, and general massaging to make it usable. The time-consuming nature of this step frequently deters analysts who may prefer more analytical work. Unfortunately, it also prevents the organization from using maybe the messiest but the most valuable data to predict the future.
We start by reviewing available variables, picking the right ones, and completing random sampling and random data testing. We bring in a random set of data with predetermined answers and test it in the sandbox. We start with several potentially suitable models that we know are likely to be predictive given your industry, your use case, and the available data.
The science of predictive modeling also requires knowing what models result in the highest accuracy and have the potential for optimization as more learnings transpire. We select and combine several models to deliver you the highest prediction accuracy, resulting in more efficient project delivery. It may include regression models, decision trees, neural networks, marketing mix modeling, and so on.
Initial delivery can result in a lift, but the higher accuracy arrives after the model can learn and undergo multiple optimizations. We try to answer the question at this stage is, “if we add more data points, how would accuracy improve?” If added data does not result in incremental gains, it is time to give the model a test run.
The hardest in project delivery is waiting and letting the models collect enough data to determine their accuracy and gaps. We run the models for a couple of months to collect enough data. Usually, our clients need models that are 80%+ accurate. In the end, we implement final changes to boost the accuracy, such as refining data, changing the model, or adding more or different variables.
Our team will work with any advanced analytics tool that your organization uses now or might recommend buying supporting technology. If you do not have any, we use multiple tools like R, Hadoop, SAP Hanna, and others and apply Python skills as needed.
Predictive Modeling is not a one-time project – you cannot set it and forget it. You will need to invest in model evolution.
Initial model development starts the cycle of maturity and eventual accuracy decline. Over time, as your business and environment change, you launch new products or retire old ones, or your objectives and KPIs change. Some businesses change faster than others, but all models eventually need revision and upgrades.
We can refine, update, and retrain your models to prolong the impact of your major investments.
Would you like to be a step ahead of your customers and competitors?
Want to turn from reactive marketing to proactive?