While meshing e-commerce and CRM platforms has been an emerging implementation idea for several years, 2018 will be the year in which businesses make some of those implementation leaps and reap the benefits, according to insiders watching the technology.
Mobile becomes the front door
Joe Stanhope, vice principal and principal analyst, Forrester: "For 2018, I think marketers will pivot around mobile. It's different from the year of mobile from the standard prediction trope of previous years; it's the year mobile becomes the starting point for marketing.
"Mobile has often been treated as a separate effort apart from marketing, or as a bolt-on set of touchpoints that are secondary to so-called primary channels. But we're seeing strong recognition at brands that mobile is, in fact, the tip of the spear for customer engagement rather than an ancillary option.
"Mobile renders many preconceived notions of digital vs. offline, or acquisition vs. retention, irrelevant. Next year, brands will start architecting their customer engagement strategies based on the assumption that experiences start with and exist primarily on mobile. This has broad implications for web design, content development, messaging and advertising.
"Collaboration between marketing and enterprise architecture will be critical for operating in a mobile-first world. The marketing technology stack, data and content will need to be fully transitioned from typical batch- or audience-oriented availability to real-time, on demand, transactional, atomic delivery of experiences that [are] characterized by mobile devices."
Strengthening e-commerce and CRM
Jeff Nicholson, vice president of CRM product marketing, Pegasystems: "In 2018, e-commerce will be about brands keeping up with fleeting moments of opportunity. Customers' expectations for immediate gratification have only been increasing. And if they can't get it from you, or you can't sense that need, they will move on and complete their purchase elsewhere. Business will need to have the ability to detect the need in real time -- and react in real time -- in order to garner that business. This raises the stakes for real-time capabilities for AI-based decisioning in the moment across all digital touchpoints.
"Most businesses are saddled with extensive legacy IT infrastructure that was simply never designed to work in real time, much less with other e-commerce and marketing technology. These systems need to be able to detect the moment of need, assemble other data from across the enterprise, make complex analytic decisions and react -- all in real time. Latency within one of these processes can result in missing the moment of opportunity altogether.
Jeff Nicholsonvice president of CRM product marketing, Pegasystems
"Today's customers move across channels at a rapid rate, along with their context, and they expect it to be connected [with a] Customer Decision Hub-style strategy, where decisions are made centrally across all channels rather than within any one channel alone. This provides a greater vantage point with which to employ these real-time decisions.
"Also, make sure all analytic decisions are made not upon analytic scores that were concluded and batched up the night or week before the interaction occurred, but instead rescored in real time, using up-to-the-second data."
The arrival of the subscription economy
Martin Schneider, head of corporate strategy, SugarCRM: "People don't buy things anymore, they join them -- meaning that we have seen the arrival of the subscription economy, full swing. That in itself is shaking up digital commerce and will drive new CRM design. Simply put: Customer retention and satisfaction are more important than ever.
"The CRM systems we have been building for years have focused primarily on new business, with a side of customer support -- typically in a silo of a call center or help desk tool. This must change. As workflow and other analytics become both more sophisticated and easier to consume for the average user, CRM must include more insights into customer health, likeliness to churn and steps an employee can take to ensure a win-win growth of customer lifetime value over time."
Use behavior analytics to understand e-commerce and CRM
Ben Harris, CEO, Decibel Insight: "In 2018, retailers will increasingly recognize the need to understand the why behind customer and prospect behavior on their e-commerce sites in order to better meet consumer experience expectations.
"To that end, they'll add marketing technologies to their toolkits that enable them to track user behavior, like scroll engagement, multiclick, and copy and paste so they can uncover how users are interfacing with the website, unlocking any potential frustrations -- like not-working links -- and improve the site's usability and lead to more online sales. This insight will also help brands understand how they can improve the entire customer journey on their e-commerce sites to decrease online shopping cart abandonment.
"Digital commerce marketers and the engineers who power the sites should pay attention [to] the data that their digital experience analytics technology provides on a daily basis. The technology will track and alert [you to] patterns in digital behaviors -- like certain mouse movements, device rotations, click rates, load times, etc. -- that signal when users are frustrated, engaged, confused, or even cases of potential fraud. By staying on top of these behaviors, digital commerce teams have [a] better chance at getting ahead of potential problems that may cause an incomplete sale -- e.g., if a link to purchase an item is broken."
Keeping up with Amazon presents new challenges
Anthony Smith, CEO and co-founder, Insightly: "The Amazon effect has changed consumer buying patterns forever. Before, consumers made decisions primarily based on quality and availability. But today, the market presents consumers with a sea of options to choose from in almost every category imaginable. This uptick in choice, along with the proliferation of subscription-based business models, is changing how customers select and interact with products and services.
"In 2018, CRM will evolve unique features beyond the scope of traditional tools -- relationship graphs, relationship scores and relationship builders will orchestrate relevant and meaningful interactions at every stage of the relationship lifecycle, from the very first touch to the final interaction.
"Architecturally, this presents a challenge. Utilizing this vast treasure trove of data is no easy feat -- especially for regular businesses who don't command an army of data scientists. The vast shift in customer expectations, increased competition and new sources of data generated by every customer interaction requires a new set of tools to help forward-thinking companies develop deeper and longer-lasting customer relationships."
Data from e-commerce and CRM improves recommendations
Devavrat Shah, Jamieson Associate Professor, MIT: "The primary goal of a recommendation algorithm, is to identify true demand of a product, a movie or, more generically, an entity of interest for a given individual or, more generally, [for an] organizational unit. For example, in an e-commerce environment, interest is in predicting demand or preference of a product for a consumer visiting the e-portal. Or, in a brick-and-mortar retail environment, interest is in predicting demand of a product for a generic consumer walking in a given store in Manhattan.
"Ideally, one wishes to predict this using historical data, as well as the available context. However, the data in a typical environment is extremely sparse. This is particularly challenging, as the underlying problem is extremely high dimensional.
"For example, a large retail marketplace may have tens to hundreds of millions of distinct products and hundreds of millions of consumers. This necessitates the need for using all sorts of data about consumers, products and types of interactions across time and space to be able to combat the sparsity and high dimensionality of [the] underlying problem. CRM data is particularly suited for enabling this, and modern development in statistical and machine learning can enable this."
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