In this article with Retail Leader, I shared my thoughts on when retailers analyze a customer's unstructured data coming from social feeds and online history, leverage those insights to create personalized promotions in real-time, and track the redemption of offers in-store and see the full revenue impact of a campaign - it becomes the marketing Holy Grail.
While this level of omnichannel seamlessness may seem difficult to achieve, it’s actually easier than most retailers realize once they define the business processes and technology required to leverage the mountains of unstructured data at their disposal.
Today's consumers expect a personalized shopping experience, and so it is up to retailers to either meet these expectations or risk losing potential interactions. According to Oracle Retail's Loyalty Divide study, only 32 percent of consumers reported that the retail promotions they receive are relevant, yet 69% said personalized offers based on their preferences is appealing.
Not only are retailers struggling to deliver relevant, personalized offers, but they are continuing to grapple with major changes in their business models due to adoption of online and mobile shopping, and increased competition from online retailers and marketplace disruptors. It’s more important than ever for retailers to adopt a data-led approach and operationalize advancements in retail science, including machine learning and artificial intelligence, to effectively and efficiently personalize their relationship with consumers on an individual level to drive top-line revenue and deliver exceptional customer experiences. Check out this quick clip about offer optimization.
Unstructured data isn’t a new concept but, until recently, it was extremely costly and time-consuming to analyze this data, often with minimal return. However, innovations such as elastic cloud and improved computing power, open source resources, machine learning and artificial intelligence have made it easier for retailers to leverage data to improve processes across the retail enterprise, particularly in customer analytics and marketing strategies. There is more unstructured data in existence than ever before, thanks to social feeds, image metadata, chatbots queries and email correspondence that retailers can – and should – be capitalizing on.
Leveraging unstructured social data for retail marketing purposes doesn’t come without its challenges. It requires a strong understanding of your overall business goals, dedication to data cleansing and, most of all, persistence.
Fortunately, cloud-based technology solutions alleviate many of the problems that once made analyzing unstructured data so difficult, and advances in machine learning, artificial intelligence and voice recognition will continue to improve these processes. Retailers who take advantage of unstructured data, whether it be social, image meta data, email or text messages, will be able to better capitalize on trends and offer targeted promotions, down to an individual level, that drive brand engagement, improve customer experience and increase the company’s overall sales.