How Enterprises can drive Sales + Engagement with AI

mediumThis post was originally published by Nabeel Adeni at Medium [AI]

Marketing Sales and Delivery – Image by FirstAlign

As an enterprise leader, you are busier than ever today, ramping up your sales and marketing machinery to reach the right customers at the right times — with the right messages and offers. Despite the headwinds from COVID-19, the central business tenets (read: KPIs) of customer acquisition, retention, and expansion (up-sell /cross-sell) remain unchanged. What has changed is their priorities for the time being. While the priority of the last quarter has been more on retention and cost savings for most enterprises, it should gradually change to acquisition and expansion (growth) in the next two quarters.

With emerging technologies, advanced skills, and constant change in customers’ behaviors and preferences, the organizations, and individuals that will continue to thrive are the ones that continually adapt to these shifts. Industry practitioners have been talking about these shifts in technology, skills, and customer behavior for a while; but as we enter the post-lockdown phases, the “social chatter” around these shifts has been further amplified. In this forward-looking article, I will explore how enterprises can use AI to drive sales and 1:1 customer engagement (while delivering omnichannel customer experiences and maintaining brand relevance).

Every enterprise is challenged by having a true understanding of their customers and knowing what message to push and what action(s) to take with them next. The larger the enterprise, the larger the challenge. Enterprise marketing and sales teams should already be discussing post-COVID-19 customer engagement strategies as part of their OKRs for the upcoming quarters.

‘Driving 1:1 customer engagement at scale’ was a far-fetched idea in the past. Not anymore now. The following accelerants have made it possible and viable to deliver best-in-class customer experience at an individual level:

  • Customer-centricity: Firstly, we are living in the Age of the customer, where your customers use multiple channels to interact with your brand, spend more, and have access to more information about you, than in the past. It is also important to note that your customers are just two swipes away from choosing your competitor. This calls for operating at scale to offer customer-specific products/services across channels while maintaining brand relevance
  • Data: The volume, velocity, and variety (3Vs) of data continue to grow each second. Customer data at large enterprises, like yours, gives a unique opportunity to analyze and monetize your transactions, interactions, ERP, telemetry, marketing, surveys, blogs, and macroeconomics.
  • Artificial Intelligence: AI has gone mainstream, bolstered by the computing power of the cloud, offering enhanced analytical capabilities to measure every customer’s engagement with your brand and products. The rise of advanced algorithms and tools has made customer segmentation and next best actions/offers possible. AI can make sense of all this by providing deep insights at an individual customer level.
  • Marketing spend: Before COVID-19, Martech accounted for 29% of the total marketing budget, as per Gartner, That’s currently the single largest area of marketing investment, greater than talent (24%), paid media (23%), and agencies (23%).

The bigger the brand, the more interactions on the Internet. The more products and services, the more digitally recorded touchpoints with customers. That’s a lot to take in — but the good news is that all these events create digital footprints that can be filtered and sorted into unique individual customer journeys. All that data, from transactions to marketing communications to social media, even macroeconomic data, can all be used by AI technology (especially machine learning) to help businesses understand their customers.

The aggregation of all the digitally recorded events in the form of a comprehensive customer journey is quite a phenomenon, cutting across historic business ‘silos’, including sales, marketing, support and giant transactional engines in large-scale enterprises, which are usually bucketed under the Enterprise resource planning (ERP) label. Experts use different terminology for Customer Journey Analytics (CJA), but in the end, everyone, including your CFO, would have bought into “one customer journey cutting across traditional business silos” being a total game-changer.

Building an all-encompassing solution to drive customer engagement at scale takes years. However, AI and a data-driven approach are now giving the enterprise the means and ability to understand their customer engagement in a much more profound and timely way.

It all starts with the data– by leveraging your own customer data to build individual customer journeys for millions of customers. For this, you don’t require “all the data you have”, but just the right amount that helps move the needle. This would help you measure customer engagement uniquely and drive improved financial returns. Proving the case for wider implementation. Here are some key things to remember as you start the process:

  • Not all KPIs are the same. You need to be able to concurrently target multiple KPIs that matter for different teams while negating conflict among those very KPIs.
  • Messages and offers can only yield results if they are relevant, timed, and sequenced.
  • State-of-the-art data science can help you segment customers and identify the right ones to send {set}s of compound campaigns to expedite the purchase.
  • With all this advanced analytics action happening across the enterprise, you will need to be mindful of data security issues, privacy concerns, and operational glitches.
  • In the enterprise, it’s not enough if you just do the data science piece. To close the solution loop (that goes from data to decision to delivery), there needs to be a fully functional user interface that your marketing, sales, service, and success teams can use to view data-driven insights and engage with customers. One such solution is Cerebri AI’s CX Platform.
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This post was originally published by Nabeel Adeni at Medium [AI]

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