Why chatbots are failing marketers by not getting personal

When Facebook announced at its F8 Developer Conference in April that its Messenger App would feature chatbots, it signaled an important shift in the way businesses can interact with customers. Chatbots are not new, but when a giant like Facebook brings the technology to the masses, companies start strategizing how they can incorporate chatbots into their operations. And thus starts the frenzy, where businesses are giddy about the promise of chatbots being the next big thing for customer experience.

Chatbots are commonly used to augment marketing and sales teams by communicating with customers — helping answer questions, increase up-sell opportunities, place orders, etc. It’s not much different from when you call your bank and get an automated response. Chatbots achieve these tasks by interpreting human speech, searching their database for a response to a question that at some time has already been answered, and delivering an automated response that will most likely meet customers’ needs.

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That’s all well and good, impressive even, but customers want better experiences when interacting with a brand. They want to feel special; they want to feel like an individual. A machine can only truly personalize interactions when it considers all data, including real-time and behavioral data, to create experiences customers expect.

I came across an interesting article from Topbots’ Adelyn Zhou, who examined how chatbots are transforming marketing. As CEO of a company that works with organizations’ marketing teams to improve customer experiences via data, I took immediate interest in Zhou’s article.

The article explains how chatbots can help marketing efforts by increasing engagement, bringing a brand to life, and even presenting personalization opportunities. All great stuff and all key factors in successful marketing efforts. But I’d be remiss if I didn’t delve into the personalization aspect.

The personalization described in that article is based on broad segments — chatbots ask basic, canned questions that lead to basic responses. This isn’t personalization.

Forrester’s recent report on “The State of Chatbots” found that most chatbots are disappointing consumers with poor user experiences by not setting expectations and acting in unexpected ways. The analyst firm also found many instances where a chatbot offered a quick and effective answer to a consumer’s question. However, about one-third of the time, existing chatbots either failed to complete the consumer’s request or provided a clunky, awkward experience.

Is this how you want your customers remembering your brand?

Today, companies collect, analyze, and make use of customer data from more channels than ever before — using website history, mobile, social, location, and CRM — which, in theory, should make a company smarter about its customers. But most companies still struggle to interact with customers in a relevant manner — making the right offers to the right customers at the right time. This is because traditional marketing segments based on factors such as age, gender, and location simply don’t lend themselves to the personalized experience customers now expect.

If you’re working with, say, 10 static segments on more generalized information, responses from this type of marketing program can’t compare to the ROI of campaigns that let you connect with more contextually relevant and timely programs. Connect when the customer is ready, not based on the company’s marketing schedules.

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Successful customer experience requires the anticipation of future needs — looking at behavioral patterns, market trends, and user experiences for proactive measures to secure a personalized, unique, and memorable experience across multiple channels. This, in turn, enables the customer to feel understood and valued, and is likely to develop loyalty — a good basis for customer retention, upselling, and cross-selling.

To achieve this, companies must go beyond placing customers in aggregate categories and instead get to know them at the individual level, based on preferences derived from all available data sources. No more hunting and re-hunting in pools of raw interaction data, no more batch processing or broad, static segmentation exercises — companies need to have access to thousands of relevant metrics for immediate action.

Marketers achieve greater results when they work with more detailed and dynamic information on individuals, rather than treating people as members of broad segments at a single point in time. The same can be said for chatbots.

Companies that focus on the individual customer’s behaviors over time, through machine learning, discover that their marketing campaigns become more effective because they deliver offers based on the actual propensities of those customers. Monitoring how a customer’s behavior has evolved over the course of their relationship with your company is critical to understanding which offer is the right one to make at the right time.

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Artificial intelligence and chatbots can be effective, and we’re on the cusp of seeing machines do superhuman things. If only they could work based on all the data from the individual customer, including real-time and behavioral. Otherwise, companies will fail to deliver the relevant experiences customers have come to expect.