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6 Key Questions - Predictive Analytics for Customer Service

Solving a business-critical question with off-the-shelf predictive analytics applications is the most straightforward path to deriving actionable insight and value from your company’s data. Predicting which customer is likely to churn or which lead is most likely to close can help you prioritize your business practices, bringing a personalized-level of attention to your customers and improve the bottom line. But in making your buying decisions there are several important questions to consider.

1. How long will it take to implement?

Analytic capabilities get only better with more and diverse data that informs the questions you are asking. Do you have access to the appropriate data? If not, how long will it take to get access? More important, what engineering work will be required on your end to get the data into a format that the application needs? You’ll be surprised just how much handholding can be required to prepare and ingest data and then put the results of the predictive analytics solution into your existing workflow. What good are intelligent predictions if they cannot be consumed in the workflow where those predictions actually lead to real value? Every additional day required to get analytics solutions into production is a day of unrealized gains.

The suite of AnswerIQ’s predictive analytics solutions make it dead simple to connect to data sources you are already using—like your CRM data sources and enterprise database systems—applying modern authentication and security processes to connect and ingest data. And since our machine learning framework builds on lightning-fast learning algorithms we build predictive models in far less time than possible with other approaches.

2. Is the model unique to you?

Every company is different. So is their data and the questions that they ask about their data. When looking for a solution to solve a particular question you’re asking, you should know just how unique the models are to you and your company. Many predictive analytics solutions deploy a universal model for all their clients and this results in low-quality answers: what constitutes a good sales lead for your company may be a bad lead for another.  To be truly data-driven you must use predictive models built to answer your questions, not someone else’s. AnswerIQ’s approach is to learn a model on your company’s own historical data that is unique and specific to your question and data. So you always get the best answers, optimized to deliver the most value to you. 

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3. Does your model improve over time?

Even if a model is uniquely determined by your historical data it is a static snapshot of where your company was, not where it is going. Often, what constitutes a good answer will change in time, as your product offerings change, your client base evolves, and the macro-economic landscape shifts. The result of today’s answers should be incorporated into an evolving model. AnswerIQ’s approach is to learn from your historical data and then continue to learn as new data rolls in. That way you always have an up-to-date model predicting the things you care about.

4. Can you customize and extend your models?

Once you get your initial question answered with a predictive model, what comes next? Perhaps you’ll want to bring in more data sources to improve the quality of the results. Or you might want to start asking new questions of the same dataset. Point solutions, by their very nature, have difficulty adapting to new accommodating new data and new questions. AnswerIQ, in contrast, has a unique technology framework that allows you to customize and extend your models so you can maximally leverage your data and your time.

5. Can you understand the drivers of the predictions?

Good predictions are indeed a prerequisite but predictions do not tell the whole story. Knowing not just what but why a certain prediction is made can help you understand your next best action. And in some industries, such as credit scoring, explainability of predictions can even be a regulatory requirement.  Most advanced machine learning approaches make it difficult, if not impossible, to tease out the why of predictions. But at AnswerIQ, we have innovated an approach that gives you a deep understanding of the drivers of predictions.  We call this “instance-level signal importance” and it’s a revolutionary addition to machine learning approaches to predictive analytics.

6. Can you explain and communicate the prediction solutions to the stakeholders?

Put into production workflows, predictions can lead to vast efficiency improvements especially when consumed and acted upon automatically (ie. without people in the real-time loop). But often times, the stakeholders of the workflows will want to know the effects that the predictive analytics solution is having on the bottom line. Rather than just provide the answers, each AnswerIQ application offers a sophisticated roll-up of relevant predictions and the impact that those predictions are having on your business process. Viewed as beautiful infographics or informative presentation slides, we help you explain and communicate the solution to stakeholders.

We'd love to hear from you about these 6 questions and more: what did we miss?. If you're interested in learning more about AnswerIQ Applications for Business please contact us, or download our Case Study below. 

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Topics: Machine Learning, Data Science, Predictive Analytics

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