Data has a lot to tell brands about the products they build and sell. Big data is illuminating in the research and development phase, shining a light on consumer interest signals that can help companies get ahead of the demand curve. But what about when the products leave the manufacturing site? What about when they’re in the hands of consumers? Big data has a lot to say then too — to those who listen.
Once the product is in the distribution chain, companies can gain insight to improve sales and marketing operations by analyzing big data gleaned from the internet as well as brick and mortar retail locations, using web analytics and point of sale information. Companies that sell products that involve a gatekeeper, e.g., pharmaceuticals, can also gain insight from doctors, caretakers and others.
Increasingly, buyers conduct research online before they purchase a product, using sites like Google, Facebook, Twitter and YouTube to find out more about the product before making a decision. Companies that take advantage of the information available from those sites, such as Google Analytics, can learn a great deal about their potential buyers and their interests.
Taking a deeper dive by integrating analytics from social media and other online sites frequented by customers prior to purchase with the company’s own internal marketing and sales spending data can reveal additional critical business knowledge. Integrating data from multiple sources can provide information that drives marketing and sales strategy changes, including optimizing campaign types and sales platforms for greater impact.
Point of sale data reveals what products are selling and where sales are most (and least) robust, among other important facts. But it can be a challenge to compile point of sale data in a usable format because sales outlets typically do not have a single standard to regulate what data they collect, and the systems and applications used to gather data aren’t necessarily compatible.
A well-designed, cloud-based data integration and management platform enable users to aggregate large amounts of input from a variety of sources, harmonize the data and provide the integrated information in a usable format. This approach gives marketing and sales teams the analytic fuel they need to make more informed decisions.
But that’s not the end of the story: Even when the product is in the hands of consumers, data still has an important tale to tell. Brands can track how their products are used in the real world and learn much about how they can refine manufacturing, distribution, marketing and sales techniques. The challenge is capturing that data, which often comes in unstructured form.
As an example, consider the information about pharmaceutical products residing in clinical notes embedded in electronic medical records. The most relevant content from a manufacturer’s perspective is typically found in a free-form comments field, where a physician might report descriptions about product efficacy for various symptoms or dosage amounts.
It’s prohibitively expensive and time-consuming to capture this information from unstructured notes manually. But using a platform that includes a Natural Language Processing (NLP) tool enables pharma brands to identify relationships between terms and map them visually, clarifying issues such as which symptoms the product was used to treat and patient adherence to prescribed treatment regimens.