Customer-centric messaging and experiences require smart responses managed by AI. If customers aren’t receiving relevant emails or other communications, they’ll simply opt out.
“Right now, marketing is failing more than it works,” said Matthew Camuso, product marketing manager for CRM software company Pegasystems, at The MarTech Conference. “And if you think about why this fails, it’s because most of what we push, and when we push it, has no real relevance to the consumers.”
He cited an internal study of over 5,000 consumers that found that 68% of them don’t believe brands care about their needs.
If brands aren’t responding to customers’ needs intelligently with an AI tool, they are usually depending on prepackaged messages sent to clusters of customers, broken down into segments. The risk is that many customers within these segments will find the messaging irrelevant and get turned off to the brand.
Risks in traditional segmentation
In a traditional campaign, a customer base is divided by demographics, geographies and other categories to create segments, and even finer-tuned microsegments. Marketers can then add rules based on what is known about the customer, like their financial means or the interest they expressed on a particular webpage. Predetermined messages are then sent to this fixed group of customers.
“At the end of the day, you’re really just trying to find the best list of people that can buy your product, and then once you get that list, it’s a little smaller, but you target all those people across channels,” said Camuso.
This strategy generates a low percentage of sales, typically in the 1%-2% range, he said.
“That may hit your short-term campaign goals, which is great, but over the long term we’re so product-focused and sales-focused that it actually takes a toll on our customer relationships,” Camuso explained.
AI-driven customer-centric experiences
To drive customer-centric messaging and experiences, AI can be used to ingest customer signals from all channels, update the customer profile, and then propose next best actions that are relevant to those customers.
“What you need is a brain, a centralized decisioning authority, that can power all of your engagements and bring them together,” said Camuso. “What that brain will do is collect data about the customer from your channels, like email or web or mobile, and then combine that with historical data you have from a customer’s profile or interaction history, as well as anything that may be streamed in, in real time.”
Instead of a cookie-cutter campaign targeting a segmented list of customers, the communications managed and authorized by AI deliver personally relevant offers or suggestions.
Marketers also play a part in the AI decisioning by establishing criteria that are specific to their industry or product. For instance, if the brand is a bank, they can only sell a credit card to a customer that is 18 or older, and that rule should be added to the set of conditions for contacting a customer.
Within these narrower conditions, the AI can then arrive at next best actions, based on the desirability of the action to customer – designated in the algorithm as a “P” value (propensity) – and also based on the value (“V”) of the action or sale to the company.
Additionally, the AI will use internal business factors to reach its decision on the action. For instance, if a product has low inventory, the AI might determine that it’s better to put another product in front of the customer where there is higher inventory, so that the company can fulfill the sale.
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AI-driven customer journey orchestration
Every new piece of data that comes in about a customer triggers the AI to recalculate the score, keeping all messages current and relevant. A credit card purchase, for instance, could trigger a timely text, or even a helpful phone call from a live rep.
These message-based next best actions are an important part of the customer experience. But, the AI decisioning can also be used to build web pages and mobile experiences tailored to the customer based on the updated information in the customer’s profile.
Additionally, a customer-centric approach powered by AI can enable customer journey orchestration that is also more relevant and effective.
Traditional customer journeys are mapped out in straight lines using rules to push customers from one action or stage to the next. When a customer – any customer – does a specific action “X”, then they are moved forward in the process to another action, “Y,” based on the basic rules of the journey.
The difference with an AI-driven customer journey orchestration is that it can remain customer-centric and choose from a larger number of options that make sense to a specific customer based on the customer profile.
When the AI is guiding the journey, it can manage more complicated journeys that include many variables, which are all scored according to propensity. A good example for financial companies is a mortgage, which is a complicated process with a number of stages that are based on many personal data points.
“Ultimately it’s just trying to help customers achieve whatever they’re doing in that stage,” said Comuso. “And when they get to that step, we never try to force them to the next one. Instead, we look across all those journeys [from previous customers] and see where she is in the bigger picture, and then use real time decisioning and propensity modeling.”
Taking advantage of the scale provided by AI modeling, the goal is for every journey or message to be fresh and relevant to the current customer. If this goal is met, customers will have the impression that the brand really does care about their individual needs.