Phase I: Instrument Your Sellers

Phase I is about getting better data, and we do this by instrumenting the team. Instead of having the team use their desk phones, we have them use a connected system to make phone calls. Instead of letting emails go back and forth under the cover of night, we track them. With phone calls and emails automatically logged -- even the calls and emails that go unanswered -- the data becomes pristine and useful to AI. 

Phase II: Optimize Your Buyers

With clean data, CRM now becomes useful to our AI efforts. AI wants basically four categories of data:

Descriptive data--who are the prospects, and what characteristics do they have?
Activity data--what actions have been taken? (calls, emails)
Contextual data--what are the prevailing conditions at any given time (weather, economics etc.)
Results data--what have been the outcomes of our activities?
Phase III: Optimize Your Sellers

For the sales leader who wants to build a team that can reliably hit quarterly targets, it’s important to understand who they have in which seats. AI can instantly evaluate the historical performance of thousands of reps, identifying the behaviors and attributes that separate one group of performers from the next. Phase III relies on data from Phases I and II, but it flips the unit of analysis from the buyer to the sales rep.

Phase IV: Custom AI

With good data on buyers, good data on sellers, and good data on how opportunities make their way through the sales cycle, the remaining opportunities to leverage AI for revenue lift fall into the catch-all category of “what else?” These answers can be broken up into two categories:

More data / data sources
Different questions

It’s imperative that sales people can monitor (and then adjust) the likelihood that a sales opportunity will close to maximize profitability. One way to do this is through integrating natural language processing (NLP) techniques (a field of AI centered around interactions between computers and humans) into a cloud platform. An example of this is Einstein Opportunity Insights on the Salesforce.com cloud, a feature which monitors for key words to help minimize risk and secure opportunities. However, any CRM leader can (and should) integrate NLP into their own cloud platforms, looking for items including:

1) The presence and/or absence of a positive/negative term via digital communications between the prospect and the sales rep. Identifying specific terms (or lack thereof) can help make clearer where the prospect is within the decision process.

2) The amount of time that passes between communications among the customer and the sales rep. Monitoring the cadence of interactions can help reps determine how often to contact a customer to generate the most favorable outcomes.

3) The presence and/or absence of competitor research by the prospect via monitoring of digital communication channels. Understanding who else the prospect is considering doing business with is another way for sales reps to better target and course correct their outreach.

AI and its underlying technology building blocks are top of mind for enterprises today. Forrester Data reveals who is driving its adoption.

AI is not a plug-and-play proposition. Unless firms plan, deploy, and govern it correctly, new AI tech will provide meager benefits at best or, at worst, result in unexpected and undesired outcomes. If CIOs and chief data officers (CDOs) are serious about becoming insights driven, 2018 is the year they must realize that simplistic lift-and-shift approaches will only scratch the surface of possibilities that new tech offers.

*AI will reshape analytic and business innovation: A quarter of firms will supplement point-and-click analytics with conversational user interfaces, and AI will make decisions and provide real-time instructions at 20% of firms.
*Big Data environments will evolve or suffer the same fate as yesterday’s data management:  One-third of enterprises will take their data lakes off life support in 2018, and half of firms will adopt a cloud-first strategy for big data analytics.
*Firms will remake traditional data and analytic roles to activate insights: Two-thirds of firms will create customer insight centers of excellence, and data engineers will become the new hot job title in 2018.
*The insights market landscape will become as complex as three-dimensional chess: The IaaS (Insights-as-a-Service) market will double, with 80% of firms relying on insights service providers for some portion of insights capabilities in 2018.

1 . CRM is declining. 

2. The #FourthIndustrialRevolution. Salesforce CEO Marc Benioff spoke at length about the Fourth Industrial Revolution, in which technologies such as mobile, artificial technology and nano technology are converging in ways that create, by his estimate, 3.3 million jobs and $859 billion of additional GDP by 2022. Conversely, however, the nature of work will be shifting dramatically, which will have a profound impact on many within the current workforce. It will be vital within this transition to help the thousands or even millions of people who will need to gain new skills in the burgeoning arena of tech.

3. #Playmakers are everywhere. In a nutshell, playmakers use data, science and smart applications to win at the sales game and to achieve revenue outcomes (career progress, too) at a level that has never been an option before. Playmakers don’t take guesses, but take strategic actions to increase and accelerate their conversations with ideal sales leads. They follow proven game strategies, assisted by AI sales data, to accelerate their success. In fact, we used the show as the launching ground of our newest features in sales acceleration, the Predictive PowerDialer and Predictive Playbooks, to make these aspects of AI available for every salesperson, to equip them with the best possible tools.

Marketers have more insights-related tools at their disposal that facilitate true data-driven decision making but AI is needed to help integrate across tools, datasets and platforms.

The nature of marketing work is changing, but not necessarily becoming completely technical and focused on data science. Instead, the infusion of AI into marketing work can aid decision making and automation can free up valuable executive time.

Consumers are changing due to AI-powered tools and devices being involved in consumer search, choice set construction and purchase decision making, and as this becomes more widespread and feels normal to consumers, AI will start to automate certain consumer decisions.

Visionary marketing leaders need to understand AI and how it impacts both marketers and consumers. Moreover, they must think broadly and creatively about the AI-powered future of marketing and take proactive steps to ready themselves and their organizations for the future that they (and their customers) are creating.

With the seismic shift in buyer behavior, we need something that goes beyond the limitations of CRM. We need to create an “AI System of Growth” that helps sellers sell in the way buyers buy. This requires three components: 1) Neural Data, 2) Sales AI and 3) Sales Engagement Apps.  

Neural Data

The modern approach to optimizing sales starts and ends with Neural Sales Data. Neural Sales Data, simply put, is human behavioral data with outcomes. For optimal success, it must be global and cross-company as well.

Sales AI

The math behind AI is becoming commoditized through companies like Google and Amazon, who give away algorithms and access to machine learning platforms. What they don’t give away, however, is the unique data that each organization (or sets of organizations) must obtain and compile on their own. 

Sales Engagement Apps

With Neural Sales Data interpreted by Sales AI, salespeople benefit from the collective experience of all salespeople in the system. They complete the circle by using sales engagement apps (email, phone, social, etc) that continually feed new data back into the system, allowing it to listen and adjust as the sales process progresses.  

• "Marketers have more insights-related tools at their disposal that facilitate true data-driven decision making but AI is needed to help integrate across tools, datasets and platforms.
• The nature of marketing work is changing, but not necessarily becoming completely technical and focused on data science. Instead, the infusion of AI into marketing work can aid decision making and automation can free up valuable executive time.
• Consumers are changing due to AI-powered tools and devices being involved in consumer search, choice set construction and purchase decision making, and as this becomes more widespread and feels normal to consumers,
• AI will start to automate certain consumer decisions.
  Visionary marketing leaders need to understand AI and how it impacts both marketers and consumers. Moreover, they must think broadly and creatively about the AI-powered future of marketing and take proactive steps to ready themselves and their organizations for the future that they (and their customers) are creating."

Building AI applications is hard work, but cloud vendors are making it easy by exposing simple-to-call, pay-as-you-use APIs. Want to add real-time speech recognition to your app? There’s an API for that. Want to recognize faces in an image? There’s an API for that and many more, courtesy of cloud giants Amazon Web Services (AWS), IBM, Google, Microsoft, and Salesforce and smaller, specialized vendors like Clarifai and Indico. These APIs are here, but more are coming, with greater accuracy and functionality and they will be powering legions of new AI-enhanced applications.