Cortana …

How am I performing versus quota this quarter?

Are any key accounts at risk of being lost?

How is the sales force performing versus last year?

Who are my best and worst performing sales reps?

What is the best next offer for each of our top 10 accounts?

If you aren’t seeking answers to questions like these from artificially intelligent bots like Siri, Cortana, Alexa, Google, or other AI tools, you and your organization may be at risk of falling behind your competition … because today’s leading companies are turning to AI to answer questions like the ones above. With AI they can get better answers more quickly, and at the same time improve productivity and reduce costs.

In simple terms, AI and machine learning are taking speech recognition and all types of data processing and analytics to levels that increasingly approximate or even greatly surpass human physical and cognitive abilities. AI pulls in troves of data and looks for patterns to provide recommendations and make decisions around activities such as setting sales quotas.

Automation and AI are among the most controversial topics of the day. For customers, AI-based enhancements offer many benefits such as convenience and accuracy, but it can have a negative impact on data privacy and employment. For businesses, AI is a source of productivity gains and even competitive advantage, but it can also alienate customers when not deployed effectively.

Digital transformations are the hot topic du jour, and AI is at the center. Companies like Microsoft and Google integrate AI into their Office 365 and Google Suite software for consumer and enterprise solutions. This enables Cortana and Google to schedule meetings, create presentations, predict email messaging, and so on. They also use AI for typical tasks assigned to finance, operations, sales.

For example, companies are starting to change how they assign quotas to sales reps. They are moving away from quota setting models designed and operated strictly by humans. Now they use AI to build quotas. AI can access vast stores of historical customer and territory data, scan for patterns and performs analyses tailored for each sales territory rather than using one-size-fits-all quota setting approaches.

Sales teams are relying on AI bots to help them identify which accounts are at risk in any given quarter. This way they can set better territory management priorities and craft plans to address emerging problems before they manifest as lost accounts. They can use AI to find untapped social connections (via LinkedIn and Facebook), schedule individual or group meetings, and create customized presentation decks that are geared to address specific issues.

Marketers use AI to identify who is on their web site and translate browsing activity into hypotheses about stage in the buying journey (e.g., early information gathering versus ready to make a purchase) and what persona is active right now (e.g., impulsive versus analytical). From there AI can predict what would be appreciated most as a suggestion by the company. Would a price discount be most appealing, or would detailed side-by-side comparisons be more suitable to their journey and persona?

Many time-intensive traditional sales and marketing tasks can be greatly facilitated with AI and machine learning. It can deliver assessments and even make decisions in milliseconds … much faster and at far greater scale than is humanly possible.

What does this look like in practice?

AI-Enabled Sales and Marketing in Nigeria

Calling on customers throughout Nigeria poses a multitude of challenges. While its culture emphasizes human-to-human interaction, accommodating this norm is costly and dangerous. Salespeople and executives frequently encounter lengthy flight delays, extreme traffic congestion and roads in severe disrepair. Travelers contend with the high risk of kidnappings and attacks with armored cars and bodyguards.

Yet, the country has some of the richest natural resources in Africa and provides immense opportunities for many multinationals.

Facing daunting competition, a recent client who served many of these massive MNCs looked for help to revamp their sales process to help them retain current customers and win more opportunities in this challenging market. Negotiating these challenges was critical because Nigeria represented the biggest potential market in Africa for this company.

The client had competed in global markets for decades but still had archaic global sales practices. Nigeria was a relatively new market for them. The plan was to modernize their go-to-market strategy in Nigeria, and then scale the learnings and AI-enabled CRM across the global sales and marketing organization.

Staffing Options

As a power generation parts delivery and servicing business, our client struggled with several staffing location alternatives. First, they could use local sales offices to avoid transportation difficulties and be closer to the customer, or they could centralize sales staff into major cities that were more expensive but more attractive locations, especially for expats.

Second, they could hire local talent who live near customers, but typically have little to no corporate B2B sales experience. Or, they could hire expensive expats, who would demand higher salaries and relocation packages given the challenges and dangers of selling in Nigeria.

Our hypothesis was that AI could help this company catch up with and surpass its competition in Nigeria. More importantly, we anticipated AI could enable the company to locate all sales and support staff in a few major Nigerian cities. This way the company could hire expats as needed whenever experienced Nigerian sales talent could not be secured.

So, the first component of the strategy was to locate all Nigerian account teams in Lagos and Abuja. These large cities were the most attractive places to live in Nigeria, so it helped the company compete for the best talent within the Nigerian and expat labor markets.

Deal Classification

Instead of chasing every lead throughout Nigeria, AI software classified each account by probability of deal closure. It also identified which specific solutions and discounts to offer the customer based on past experience with similar customers or that customer.

AI-enhanced deal classification offered numerous benefits. The company could focus on deals that were more likely to close. They could avoid wasting time and expense on traveling and selling to accounts that would not close. Even better, they could increase their new deal close rates and key account retention rates.

Data Input Enhancement

The primary data used by the classification system included facts about account: geography, size, spending level, relationship tenure, relationship depth and breadth, digital sophistication, and historical buying patterns. Marketers also secured external data such as market statistics, potential customer details, local and regional regulations, etc.

Salespeople and marketers were shown how predictive accuracy increased as data was added to the system. As deal classification accuracy increased, salespeople incurred fewer trips to secure a close. This reduced travel expenses and personal safety risks. Further, sales forecasts became more accurate, which allowed the CFO, Chief Sales Officer, and CEO to create more accurate plans for the future.

Account Analytics

The account analytics offered by the AI system helped sales teams have more powerful and effective conversations with their customers when they met in person. Instead of selling parts and long-term service agreements, account managers could propose plans for driving value for the customer in areas that were most relevant for them and their unique business. In other words, AI-enhanced analytics fueled a highly consultative sales approach.

Administrative Bots

Various administrative support roles were replaced by AI bots that could help schedule meetings, annotate phone calls, search for meetings to book, identify high-priority emails, and identify new contacts. The bot we created for this client provided:

• Account information
• Contact information
• Deal classification
• Accounts at risk
• Quota information: total quota, percent of quota attainment to-date, quarter-over-quarter run rate, etc.
• Pending deals by revenue size
• Pending deals with immediate action items
• Potential leads
• Potential decision-makers (via LinkedIn scans)
• Relevant news about Nigeria, the market, and customers (e.g., mentions by analysts, quotes from executives, changes in product offerings, manufacturing or office expansions, legal activities, etc.)
• Relevant old or new posts from customers (e.g., scan customer websites for relevant reference materials)
• Proposed content for use in upcoming customer presentations.

Conclusion

The results of the test in Nigeria have already increased salesforce productivity, and year-over-year revenue growth is forecasted to increase substantially. These gains led the client to start planning for AI expansion and global roll-out. The expansions include: financial forecasting, quota allocation among reps and accounts, automation of multiple functions and tasks, integrating virtual reality, and intelligent cloud systems.

The Nigerian go-go-market success story is very similar to what other companies are experiencing with AI-enabled sales transformations. AI can be embedded into the way companies track sales operations, anticipate problems and solve them before they arise. This represents a fundamental shift in how companies sell and serve their customers. Rather than wait for customers to report a problem, AI-enabled salespeople can be more fully engaged in problem prevention and relationship enhancement.

Salespeople are much more inclined to view AI-enabled CRM as a key to their success rather than a necessary evil. They are motivated to maintain and enrich system data feeds because this contributes to their success and helps them be more productive with their time.

As AI continues to enhance CRM system across the world, it is increasingly important for companies to continue to make AI more human. AI should make customer conversations more valuable … not eliminate human interaction altogether. An even greater challenge is for AI to support human ingenuity and enrich jobs, not reduce employment or create deeper gulfs between the “haves and the have nots”.

A blended approach of AI and human interaction will not only improve performance of the people in your organization, but it will also improve operational efficiency and overall customer satisfaction.

Matt Hellman is the United States Area Transformation Leader at Microsoft.