It’s no secret that consumer expectations for personalized content have shifted drastically in the last decade, requiring brands to rapidly mature in how they engage with their customers. In fact, according to Boston Consulting Group, personalization is a game changer that will shift $800 billion of revenue to the 15% of companies that get it right over the next five years.
This evolution has impacted the retail industry more than any other. Retail once thrived solely on in-person shopping experiences, like smelling 10 candles before selecting the most enticing; trying on a shirt and asking the sales associate which pants in the store would match best; or shopping for a new mattress and laying on each one to ensure the right level of firmness. Now, much of the customer journey has become digitized, and consumers seek the same individualized experiences online.
However, some existing product recommendation tools have serious flaws that cause more trouble for the brand than good. Most of the time, product recommendations fall short of customers’ needs for truly contextual, personalized experiences. For example, shoppers do not want a recommendation for jeans they’ve already purchased or a book they’ve already read — they want to be offered complementary items, which some engines are not equipped to handle. The end result is a disjointed customer journey that risks recommending products that do not appeal to the customer or are not available in the sizes, colors or locations that buyers prefer. This could have a negative impact on the image of the brand and ultimately turn consumers off from engaging with them all together.
It’s time brands demand more from product recommendation tools — their consumers deserve it. To do so, retailers need an engine that can accurately provide their consumers with the right recommendations and offer marketers insights into the data behind the recommendations. When selecting this engine, though, marketers should avoid the following three shortfalls.
Context Is Everything, And Data Needs Context
Some marketing tools support limited data feeds that lack structure and are uploaded in batch processes or rely on a limited set of algorithms that fail to consider everything known about a customer. Other engines can fail to infuse everything known about the individual, as they don’t have the proper algorithms set in place. This ultimately means the brand cannot deliver on the promise of personalization because the information provided by the engine is too generic and cannot be analyzed or put into place based on each individual customer’s needs. It fails to accurately capture and make recommendations based on meaningful information.
To better the experience, marketers need a tool that can help them source customer data from all channels in real time and combine the data with a set of algorithms put into place by the business. Brands can then infuse recommendation algorithms with individual shopper data, including historical data and real-time visitor behavior. This level of data provides the context necessary to enable marketers to deliver the most relevant recommendations possible to every single shopper, increasing average order value, driving higher profits and building long-term customer loyalty.
Customers Need Recommendations Across All Touch Points
Beyond acquiring data from each touch point, brands also need to offer product recommendations across all touch points, including in-store, online, email, call centers and mobile apps. For marketers with baseline product recommendations, this often meant they were challenged with doing the impossible: creating truly individualized omnichannel consumer experiences in real time.
The advent of advanced recommendation engines has changed that, as marketers can now leverage a recommendation engine to drive engagement across every customer interaction, no matter the channel. Only a robust and flexible recommendations solution will be able to deliver the consistency that customers expect, across all touch points, so that businesses can drive the high-impact results seen from well-executed personalization.
Marketers Need The Tools As Much As The Customer
Ultimately, product recommendation tools are implemented to help marketers better engage customers and meet business goals. However, on top of needing to create better product recommendations, the tool also must fit the individual needs of the marketer.
Some engines lack marketer-friendly, self-service features and require significant vendor intervention. Without being able to seamlessly use the technology or have a developed understanding of the reasoning behind some of the recommendations, marketers are unable to collaborate with the technology to achieve the goals that matter most to their business. When a tool has a user-friendly interface, however, marketers can coordinate with the technology by using any combination of manually curated product selection or algorithmically-driven recommendations. Moreover, the tool should not only be intuitive, but also capable of creating in-depth analyses of prior data to help the marketer create attainable goals.
The best tools for helping marketers overcome static product recommendations can provide real-time analyses on mission-critical data, create omnichannel consumer engagements, and arm marketers with both an easy-to-use interface and the insights needed to help them do their jobs better. With personalization engines advancing, brands can provide better, more accurate recommendations and apply data in ways that initial product recommendations were never able to. The bar is set higher, and marketers need to overcome these shortfalls now or they’ll fall even further behind.
Brian O’Neill is CTO at Monetate, where he lives at the intersection of innovation and execution. O’Neill has a proven track record of applying open-source solutions to enterprise-scale data and analytics problems, specializing in big data, high-throughput, real-time systems. He began his career in Silicon Valley, but as a Philadelphia native, he has returned to the region and is dedicated to growing the innovative, entrepreneurial spirit of the city. When O’Neill is not building communities, writing code or contributing to open source projects, he is writing books. He has published and contributed to books on machine learning, real-time processing and distributed databases. This venture has led to recognition as a perennial Datastax Cassandra MVP and winner of InfoWorld’s Technology Leadership award. He is a graduate of Brown University and holds patents in artificial intelligence, data management and service orchestration.