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Advanced analytics for marketers. What to use when and why.

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3 min read
The visual artist Andrew Strauss works with algorithms and code to reimagine abstraction, repetition, and patterns through generative automation.

Key Takeaways

Marketers have more tools than ever at their fingertips to not just process customer data, but to extract significant value from it.
Yet, data from Forrester’s Customer Experience Index (CX Index™) shows that the average CX Index score increased every year from 2018 to 2021, then declined in 2022 with more softening expected in 2023.
What is causing this disconnect? A recent HBR article suggests that marketers are unsure of the ability of their techstack, don’t trust their tech to find the right answers for them, and struggle with how to incorporate AI/ML advanced analytics into their workflows.
HBR recognizes that humans are better at decisions involving intuition and ambiguity, and machines are far superior at decisions requiring deduction, granularity, and scalability.

Most brands collect far more data than they can analyze and are only able to realize a fraction of its value in their operations. Unprocessed data isn’t just a missed learning opportunity, it carries a cost of customer frustration and lost revenue.

Recent data from Forrester’s Customer Experience Index (CX Index™) shows that the average CX Index score increased every year from 2018 to 2021, then declined in 2022.  This dip in CX performance during 2022 and expected softening in 2023 presages a drop in tolerance and brand forgiveness. Brands who haven’t already, should ramp up efforts in 2023 to handle a harder-to-please customer.

Marketers have more tools than ever at their fingertips to not just process data, but to extract significant value from it. So why are they still missing this mark?

A recent HBR article suggests that marketers are either unsure of the ability of their techstack, or don’t trust it to find the right answers for them. They struggle with how and when to incorporate AI/ML advanced analytics in their workflows.

The authors identify the struggle marketers feel with when to trust the machines and when to trust their guts. They also recognize a clear difference between the two. Humans are better at decisions involving intuition and ambiguity, and machines are far superior at decisions requiring deduction, granularity, and scalability.

To help marketers understand what analytics they should apply when, they proposed a simple framework for decision-making:

Knowing that resource and time constraints are increasing at what feels like an exponential pace, Kognitiv is giving marketers time and space for the work that requires a human touch. By applying AI/ML to customer journey analytics, Kognitiv is leveraging marketing expertise to automate and take the guesswork out of converting data into insights into action.

Chief Product Officer Anthony Wintheiser says "Data without intelligence, and intelligence without optimization will do little to help brands deliver better customer experiences and increase customer lifetime value. With Kognition, Kognitiv's AI/ML decision engine, we combine all three to recommend what marketers should do to consistently and efficiently hit their business goals."

Kognitiv Pulse delivers a real-time view of your entire customer base and buying patterns at an individual customer level. Built on our SmartJourney® methodology—a forward-looking 8-step customer journey analysis of customer data—and powered by AI and machine learning, Kognitiv Pulse identifies where customers are in the buyer journey and precisely when to engage them. In gaining a holistic view of marketing efforts, tracking ongoing success, and identifying opportunities to increase customer lifetime value, brands can focus marketing spend on generating outcomes that grow their bottom line near-term and over time.

More information or book a demo of Pulse here.

Full HBR article 'Analytics for Marketers' here

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