With a first name of “Artificial,” AI has certainly entertained us with its virtual possibilities. Stories of wholesale disruption by robots and fully automated lives make for good movie material, but as of yet, AI hasn’t dominated the marketplace, consumer experiences, or business applications in a monumental way. AI has the potential to change our daily lives, yet for most, its impact so far has been nominal.
As a businessperson concerned with driving better customer engagement, you’re no doubt interested in this topic, yet probably carry some healthy skepticism about the potential for return from your AI investments, and the risk of them failing.
Congratulations! Your suspicion is not only natural, it’s warranted. Here are three tips for how to maximize value from your AI investments, and minimize any risk of disillusionment.
1. Provide predictions about Customer Intent
No doubt, you have scores of business intelligence systems that compile and codify data. They provide customer profiles, program dashboards, and other scorecard reporting of historical results. Although informative, these systems aren’t predicting anything. As such, they are rear view mirrors, providing a view of the past, but not anticipating and generating ideas regarding courses of action that may lead to more optimal outcomes.
Any investment in AI aimed at improving customer engagement must include capabilities to predict customer motivation. Why are they calling? Are they already upset? Are they highly likely to be shopping for another provider? What product or service best suits their true needs? How valuable is this customer over their projected lifetime?
Answers to these questions are always guesses, yet pragmatic AI systems today use proven statistical methods to minimize errors in predictions, calibrate themselves with feedback loops, and provide confidence intervals so users understand their range of applicability.
For example, it’s feasible today to have a portal providing your marketing employees with accurate predictions such as:
• Customer value
• Churn likelihood
• Loyalty to brand
For service agents, predictions like:
• Customer sentiment
• Reason for calling
• Nature of problem
For sales personnel:
• Price sensitivity
• Available budget
• Perception of value
Effective AI has to improve your ability to understand what impels your customers to behave the way they do, or the way they may act in the near future. Work backward from these insights, and demand that your AI systems and vendors can prove they have experience extracting insights from available data, and in predicting and surfacing these items.
2. Make dynamic suggestions to better serve the Customer
Consumers do business with brands that provide repeatable value. That value comes from not just positive product use, but also from an enjoyable and smooth buying process, a friendly and efficient on-boarding experience, and stellar service.
As consumers experience a brand during those journeys, they rack up the score, keeping tally of the relevance and effectiveness of the systems and people they encounter along the way.
Any AI system worth its salt should provide ranked suggestions either directly to customers, or to customer facing employees such as:
• Next Best Offer: The most relevant product needed, and an individualized incentive on it that will be both compelling, yet still economically affordable to the business.
• Next Best Service Action: The best thing an agent can do next to maximize the chance of reaching an effective and efficient solution to the service problem at hand.
• Next Best Sales Activity: The best action for a salesperson given available leads, accounts, contacts, and opportunities.
For the marketers responsible for providing next best offers, AI systems should help them recognize buying patterns, automatically perform tests, filter out offers that don’t apply, and statistically rank the best content & promotions for the right individuals. AI should even suggest the best timing for those recommendations.
For service workers, AI should deflect routine service requests to automated or self-service channels, guide agents on complex service cases, surface potential solutions to issues, and help gauge the sentiment of the customer during the process.
For salespeople, AI should predict the best contacts to engage with in an account, the activities most likely to move an opportunity to the next sales stage, and which accounts to spend energy on to maximize close rates and quota attainment.
3. Install a system that learns in Real-Time
Your world changes every day. As a professional, you wake up every day to news of competitive threats, new opportunities, and market conditions that vary the effectiveness of the strategies you employed yesterday.
If you were slow to react, or simply ignored these factors, you’d fully expect your overall business performance to degrade, so you listen carefully to these environmental conditions, and you adjust accordingly.
Think about your AI systems the same way. They must include adaptive mechanisms, where recommendations made are monitored, in real-time, and dispositions are fed back into the machine, so it can learn from its success and mistakes. Marketing, service, and sales systems receive feedback constantly in the form of customers either ignoring your treatments, or responding to them, so ensure your AI system uses them. Your AI system should rapidly improve its performance, as it’s fed more data, and as it tunes itself. If it’s not, after a short trial period, start asking some hard questions to your provider.
Make sure your results (even if delayed), are monitored, measured, and understood. An accurate measurement of the real business value from AI comes when you understand the baseline, and can measure the lift you get when you employ the insights and recommendations delivered by AI.
Track response rates, conversion rates, incremental revenue, return on investment, and compare to what your vendor promised, what you expected, and what you need to achieve.
AI is a broad topic, yet to improve customer engagement and your outcomes, boil it down to these 3 things; understand customer intent, make relevant suggestions, and learn in real-time so your performance improves over time. If you do these, you will realize REAL value from AI.