Every second of every day, companies are inundated by massive volumes of data from diverse sources: sensor data, clickstream data, location data, social data, video data, and so forth. But the pace at which enterprises can leverage this data to sense and respond intelligently to customers lags well behind the pace at which data is exploding. Fortunately, advances in marketing automation and artificial intelligence (AI) are enabling enterprises to respond to the data challenge.
We are at the threshold of a new era in marketing that I call sentient marketing - a vision for customer engagement that is powered by data, scaled with automation, and personalized through AI. Sentient marketing is a set of capabilities and processes that enable enterprises to create personalized customer engagement at scale and in real-time.
Sentient marketing originates from the broader vision that my co-author Oliver Ratzesberger and I wrote about in our recent book, The Sentient Enterprise: The Evolution of Business Decision Making.
A sentient enterprise can sense and respond intelligently to events in its environment. It functions like a living organism, continuously sensing and responding to its surroundings to make autonomous decisions at high speed, at large scale, and in real-time. A useful analogy is driver-assisted and autonomous vehicle technologies that free the driver from dozens of routine decisions made every minute while operating a vehicle: braking, accelerating, adjusting the distance to the vehicle ahead, etc. Or consider high-frequency algorithmic trading, which operates at a speed beyond human capabilities, pitting machine against machine, usually in ultra-short-term market positions.
The same basic concepts apply in customer engagement: when decisions are made routinely and with high frequency, automation and analytics can increase the speed and improve the relevance of engagement with customers. Although not all decisions need to be made in real-time, most marketing decisions in customer engagement can benefit from reduced latency and improved personalization.
Goals of Sentient Marketing
Sentient marketing achieves four key goals for transforming customer interactions:
- Personalized: Sentient marketing allows for engagement with each customer based on the customer’s profile, preferences, intentions, past behaviors, and purchase patterns. Essentially, everything that an enterprise knows about the customer is brought to bear on every interaction with each customer in order to improve the next interaction with the customer. Personalization takes customer segmentation down to the level of a “segment of one” – each customer being treated as a unique segment.
- Engagement: Marketing communication traditionally has been one-way, designed to reach and influence customers to change perceptions and to drive purchase decisions. With sentient marketing, however, communication is a two-way street: companies communicate with customers while customers respond to and provide feedback on their experiences and preferences. Engagement is an ongoing process, as opposed to a one-time exposure to an advertisement or a one-time purchase transaction.
- At Scale: Automation can enable enterprises to handle very large volumes of customer interactions without sacrificing personalization. Sentient marketing leverages automation and analytics to allow marketing, sales, and customer care interactions to be scaled without limit. From automated marketing campaigns to automated offer design and automated customer care, machines can scale in ways that human beings cannot.
- In Real-Time: Customers today are more demanding about their expectations for product experience, service, and responsiveness. Sentient marketing allows enterprises to reduce the latency in customer engagement, with the goal of enabling real-time customer engagement.
Sentient marketing may be an aspirational North Star for marketing organizations, an objective that is still far in the future. Yet there are glimpses of the future at work in leading enterprises today. Consider the example of customer engagement at the digital services company Reliance Jio Infocomm of India (full disclosure: I serve on the Jio Board of Directors). Jio has grown at a breathtaking pace by acquiring 252 million subscribers within 24 months of its commercial launch. In the most recent quarter (July 2019 to September 2018), Jio acquired 37 million new customers. Unsurprisingly, Jio’s disruptive market entry has spurred incumbents to respond aggressively with competing promotional offers. Customers, in turn, want Jio to respond to the offers they receive from competitors.
With over 250 million customers, it is not unusual for 600,000 to 800,000 customers to call every single day to request a better deal. Jio’s customer representatives would need to evaluate the offer, decide on a response, and present the response to the customer – a process that would completely bog down the customer contact center if every call took even 15 minutes. To deal with this challenge, Jio is working on a “real-time promotional response system,” an automated system that accesses a database of competing offers to decide how to respond intelligently to each customer. This process requires several steps. First, Jio must determine if competitors’ offers (which may be forwarded emails, SMS messages, copies of promotions, or even photos of billboards) are legitimate and still valid. Next, the company must determine whether it makes good business sense to match an offer or let a customer go. Then the company needs to design the right offer for each customer – more data, promotional pricing, etc. Finally, this offer needs to be presented to the customer.
The promotional response system goes through all these steps. First, it “machine reads” the offer and evaluates the validity of the offer by matching it against a database of current competitive offers. Then it assesses the value of the customer by looking at the customer database and uses predictive analytics to design the appropriate response to the customer. Further, the system uses machine learning to monitor customers’ reactions to the offers, so that the relevance of promotional offers can be continuously improved.
Once the promotional response system is fully enabled, the entire process will happen in real-time with zero human intervention. This is sentient marketing in action.
Moving Towards Sentient Marketing
Most enterprises today are far from the ideal of sentient marketing. The challenges in moving towards this ideal state are organizational as well as technical. On the organizational front, enterprises are hamstrung by organizational silos that are responsible for specific stages in the customer journey – marketing, sales, and customer support. Operationally, enterprises must deal with inflexible legacy systems and processes that harken back to the days of manual decision making. These enterprise systems were designed to process transactions and are ill-equipped to handle unstructured behavioral data that provides the context behind the transaction. To move towards sentient marketing, enterprises need to embark on a phased journey that involves three steps. First, enterprises need to put their data house in order by building a foundation for capturing, storing, and interpreting all types of structured as well as unstructured customer data in a “data lake.” Next, they need to deploy systems to automate the processes for managing the end-to-end customer journey. Finally, they need to harness analytics and AI tools to analyze and optimize customer interactions in ways that are beyond the capabilities of human marketers.
Currently such automated, analytics-powered customer engagements occur in small isolated pockets within enterprises. There is a lot of room for sentient marketing to grow and take hold in three key phases of the customer journey: advertising and marketing; sales and commerce; and customer care and advocacy.
Every industry has been increasing its use of digital advertising and marketing, not only for the obvious purpose of increasing sales, but also to promote brand loyalty across multiple channels. One example is the “Live in Levi’s” campaign, which began as a social engagement website with photos and videos. Today, it is integrated into Levi’s e-commerce site to offer greater customization and personalization of Levi’s clothing. Another example is Sephora, a cosmetics retailer, which recently rebuilt its customer profiles to include “360-degree data” of in-store purchases, online browsing and purchases, and interactions with sales people. The approach is omnichannel, recognizing the influence of mobile, digital, and in-store touch points on the customer journey.
Beyond digital, some companies are taking the next step to bring greater automation to sales with chatbots. In “conversational commerce,” consumers interact with chatbots that not only make suggestions in response to customer queries, but also reflect the personality of the brand or product line.
Automation can also play a huge role in customer support and advocacy. Here the interaction centers on problems—e.g.., clothing that doesn’t fit. Sophisticated, AI-powered chatbots can have in-depth “conversations” with a customer to gather information, suggest solutions, and complete a return or order. The next-generation chatbots will be transaction-enabled, which means that they will be integrated with enterprise IT systems to allow chatbots to look up inventory, search customer order history, make product recommendations, and complete billing transactions without escalating the session to a human being.
Building Sentient Marketing Capabilities
Once marketers buy into the concept of sentient marketing across the customer journey, they to build three specific capabilities:
Dynamic Content for Engagement: Customer journeys need to be nurtured with content that is relevant, timely, and useful. The goal of dynamic content is to match content with intent.
Marketers need to create and curate a broad array of content such as blog posts, videos, infographics, advertising, webinars, customer stories, and testimonials. Each piece of content must be tagged and indexed in terms of its relevance to a specific customer segment, a specific stage in the customer journey and a specific product or solution. For instance, a white paper from Cisco Systems on Intent-Based Networking may be tagged as relevant for technical decision-makers in large enterprises who are interested in improving the ability of their networks to adapt to business needs and are in the early stage of exploring solutions.
Marketing Automation for Scalability: The second capability is automated campaign management, which is essential for customer engagement to be scaled. Using marketing automation platforms from companies like Marketo, HubSpot, and Pardot, enterprises can design and execute marketing campaigns across a variety of channels. Marketing automation platforms allow for dynamic segmentation, lead nurturing, and marketing performance measurement. Automated campaigns can be continuously tested and improved in terms of the creative, the channels, the frequency, and the spend level across channels.
Analytics and AI for Personalization: Automation allows enterprises to execute faster and respond to customers more quickly. But speed is not enough. Sentient marketers need to respond intelligently at scale so that they can create personalized and relevant customer engagement. For this, they need a third capability – analytics and AI to continuously test, learn from, and adapt their customer interactions. AI capabilities need to be infused into each stage of the customer journey: attracting customers, discovering customer segments, designing customer offers, improving customer conversion, and reducing customer churn.
Consider how Microsoft is infusing AI into various aspects of its marketing processes to improve effectiveness. For example, Microsoft has developed an intelligent lead-scoring application that eliminated leads that have invalid or fake contact information. The system progressively learns which leads are fake, saving time and reducing frustration among the sales team. Microsoft has also built a “Daily Recommender” application that provides recommendations to salespeople on which products a customer is most likely to purchase based on past interactions. The application is also integrated in the content and marketing templates that salespeople can use to engage with customers, so that they spend less time researching and designing customer offers. In the customer support domain, Microsoft has created an AI-based recommendation engine to support customer service agents. While human agents chat with customers, machine learning algorithms run in the background to predict the possible problem the customer is facing and suggest diagnostics from Microsoft’s library of solutions to determine the root cause. The recommendation engine improves agent productivity as well as customer satisfaction.
As the Microsoft examples suggest, sentient marketing brings humans and machines together to create more scalable, personalized, and timely customer engagement. The journey begins with pockets of automation in support of human marketing, sales, and customer support personnel. It gradually progresses to the point where end-to-end business processes can be automated, freeing up humans to make higher-level decisions on design and strategy.
William Gibson, the American-Canadian science fiction writer, commented that “The future is already here — it's just not very evenly distributed.” Sentient marketing is also here, it just exists in small pockets within the overall customer journey and in specific channels such as social media marketing. Gradually, enterprises will expand the scope of sentient marketing to span more stages of the customer journey and more channels for customer engagement. The North Star beckons, guiding organizations to end-to-end automation of marketing, sales, customer care, and advocacy across digital and conventional marketing channels. That is when marketing will become truly sentient.
This article is written by Prof. Mohanbir Sawhney and published by Forbes Magazine on Jan 14,2019. Prof. Sawhney is a globally recognized scholar, teacher, consultant and speaker in business innovation, modern marketing and Artificial Intelligence applications in business.
This article is republished on our site with his permission.