Artificial Intelligence, Customer Relationship Management, Data

In 2018, Data Will Fuel the Emerging Insights Economy

Data Insights

The prospect of artificial intelligence (AI) changing everything generated considerable buzz in marketing circles in 2017, and that will continue in 2018 and the years ahead. Innovations like Salesforce Einstein, the first comprehensive AI for CRM, will give sales professionals unprecedented insights into customer needs, help support agents solve problems before customers even perceive them and let marketing personalize experiences to a degree that wasn’t possible before.

These developments are the leading edge of a shift that is taking place almost imperceptibly: the emergence of the Insights Economy. Just as the Industrial Age ushered in a manufacturing-focused production economy, the Information Age is driving the Insights Economy, with data providing the fuel. The best AI tools can transform raw data into actionable insights.

But it’s important to keep in mind that, while it is highly sophisticated, at its core, AI is a software program, and if the data fed into it is incomplete or inaccurate, the quality of the output will be reduced. To fulfill AI’s promise, marketers need to find a way to compile data, apply standards, update the information and clean the data up as appropriate.

It’s vitally important to be able to identify data quality and convert data into insights. While the Insights Economy is an emerging phenomenon, the fuel needed to drive it forward is clear: high-quality data. Over the coming year, more companies will implement four-step processes like this to achieve the data quality they need to generate game-changing insights:

  1. Step 1: Planning — Marketers use historical data to create plans in this step, working with sales to identify goals and determine the average deal size, lead volume and velocity needed to meet the objectives. Then, they determine conversion rates based on past performance and pinpoint what they need to do (e.g., how many leads to generate, optimal sales cycle, etc.) to meet current goals.
  2. Step 2: Achieving — At this step, marketers evaluate campaign performance to gauge their progress toward objectives and infer insights. In this way, they can convert data into insights to create a feedback loop. One example of this is the “you may also like” product recommendations ecommerce platforms provide, which are updated as new data flows in.
  3. Step 3: Optimizing — As the name suggests, this step involves continuous improvement of processes, such as the handoff between marketing and sales. As new information comes in, marketers who are optimizing processes conduct careful reviews and identify techniques they can use to improve results. Processes are adjusted, and the results are measured.
  4. Step 4: Evaluating — In this crucial step, marketers evaluate their programs and find out which campaigns generated the highest returns. They take a look at channels, messaging and other factors to determine ROI so they can plan future campaigns based on which approach proved most successful. The knowledge gleaned at this step comes from insights produced by the data.

As more business leaders perceive the shift to the Insights Economy, look for companies to start consolidating data on systems of record like their CRM platform and applying these steps. AI is an important component in the evolution of marketing, but it requires bulletproof data to work as intended, which means sales and marketing need a single source of data truth.

When sales and marketing use a common solution stack, the teams can work together more closely, using the steps outlined above to constantly increase data quality — and generate increasingly valuable insights. The ability to demonstrate campaign impact and access data on a central system like Salesforce lends credibility to marketing and enhances the team’s collaboration with sales.

So, as 2018 unfolds, companies will continue to seek AI solutions. It’s a positive step — the possibilities with AI technologies like Einstein are truly amazing. But it’s important to remember that data fuels AI. Those who recognize data’s central role and use a conscious strategy like these four steps to improve quality will thrive as the Insights Economy continues to emerge.

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