Understanding customers is a difficult task. It typically involves experts, either from in-house or outsourced, making this a lengthy and expensive proposition, not to mention that in most cases the result is just a partial understanding.

Additionally, Customers’ opinions can change quickly. There’s always a reason for change. Just to name a few reasons:

  • Seasonality
  • Market disruptors
  • Change in tastes
  • Competition

When you connect the dots, you easily see that slow and shallow is not what you need to chase a quickly moving target. You’re basically stuck in still photos when the world is in video. A recent McKinsey Survey about organizational agility confirms the fact that organizations tend to fail to keep pace with and understand customers’ changes with proper speed. The ability to quickly react to change and move toward-creating and/or protecting value opportunities is elusive for most organizations.

 

Why are we stuck with slow and shallow?

The main reason organizations are stuck with “slow and shallow”, is the reliance on experts to understand customers. You need IT people and data scientists to work with complex AI/Survey/Customer-Experience software packages to configure these and integrate them with internal systems. The method of relying on experts causes the the price of understanding customers to be very high, which results in this resource being kept centralized in the organization and used sparingly.

While these systems can issue weekly or monthly reports, the elements covered in these reports are static mostly – the topics tracked, vendors tracked, and metrics tracked are all the same topics originally setup by the experts.

Sure, you can use the experts to update the settings, but this takes time and it just means you’ve heard (as an example) about a new competitor or product in the market that you have zero knowledge about, and now you need to wait for the experts to set things up so you may know something later.

To sum it up:

  • Customer analytics are maintained by experts, making it an ongoing manual effort
  • Because of the associated cost, this is a centralized effort/group that can’t devote time to individuals or small groups in the organization
  • Metrics tracked are pre-determined and aim at the common topics of interest across the enterprise (Loyalty, price, quality, etc)
  • Any change is done in retrospect – I heard about a new competitor and now I need to see what/when I can get some info on it

 

What is the proper speed? Or how we move to Video?

There are several factors that come together to understand your customers:

  1. You need fresh data that is regularly updated (One-off surveys will not do the trick)
  2. You need automation to decipher market trends and changes
  3. Each relevant member in your organization needs to be able to do self-service analysis on the results, without a centralized service center

 

Getting fresh data regularly

With so much data available online through eCommerce sites, social media or internally through call centers and service chats there should not be any shortage of data for analysis. On the other hand, doing a one-off survey or focus group will not solve your analytics needs, as seasonality, change in taste or new competitors will not wait for your next survey, and you will only find out when it’s too late

How automation can bring depth and speed

New generation AI solutions can automatically convert messy, unstructured qualitative data into quantitative intelligence. For your AI automation solution of choice to accomplish this conversion it should ideally have these capabilities:

  1. Classify data into topics the way a market research expert would, but at scale, to cover all possible topics
  2. Handle expression variations, nuances, abbreviations, slang, covering the different ways to describe the same thing
  3. Deep analysis of sentiment, across variations, cynicism etc. to properly classify positive/negative/no sentiment

How self service can move you faster

Once deep insights are automatically and regularly available, you want all relevant parts of your organization to be able to access these insights. This means that the solution used needs to be intuitive, and autonomous. If the solution is complex or if it requires IT or any other centralized group to change or support or configure it will mean longer decision cycles and possibly compromised results. You want to be able to slice and dice the data at will and depending on your immediate needs of the day.

 

Conclusion

Today, almost all AI solutions for consumer analytics are incapable of keeping up with the speed of change of consumers. They rely on humans which means they are slow, inaccurate and can’t adapt quickly enough. To make things worse, due to cost of labor these solutions are typically managed by centralized/shared groups, which adds delays to decision/result cycles and compromises the quality of the results.

Revuze, however, is revolutionary. It is the first to offer automation in a field that is heavily relying on manual labor. It offers an innovative technology that turns customer opinions from any format into quantitative data that anyone can analyze – at will, anytime.

But what about all those humans – will they be left behind? Quite the contrary! Imagine if . . . your intelligence-focused employees didn’t have to worry themselves about manual labor and could, instead, spend time on intelligence! Imagine if they, instead had time for interpreting, analyzing, and predicting . . . how much more successful could your company be if you could actually speed up and increase the accuracy of your intelligence?

 

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