DIY vs Enterprise CXM

Enterprise Customer Experience

Between the eCommerce and mobile internet boom, the war on customer’s pocket is on at full force. Major tech vendors CEO’s have recognized it and are vocal about the need for detailed customer understanding (Customer eXperience – CX) as a means to stay ahead of the competition. 

“We’re acquiring Qualtrics so you can combine your customer’s experience data with SAP’s operational data to transform how you run your business”, said SAP CEO, explaining their Qualtrics acquisition.

“Today’s consumers have the ability to review, compare prices, and purchase nearly any product at any time. This shift in the balance of power makes it more important than ever for companies to focus on customer experience” is a recent quote from Oracle’s CEO.

Enterprise Customer Experience

The execution gap – important but not achievable?

So, leaders and followers agree that deep understanding of customers is key. Still, there’s a huge gap between importance and actual achievements. According to strategic consulting company Bain & Company, while 80% of companies believe they deliver “super experiences” only 8% of customers agree.

It seems obtaining analytics for decision making is challenging. Most existing methods today take months to deliver and are either based on consultants and traditional market research methods, or they are based on heavy enterprise solutions. Either way that means solutions that are expensive, slow and even shallow in terms of insights. This explains why with huge $ spend on market research, CX, consultants etc. still only 8% of customers see great experiences.

What are the makings of a typical enterprise CX solution?

Because today’s “best practice” to analyze unstructured customer opinions relies heavily on data scientists, analysts and IT folks to train AI machines so they can search for similar patterns in consumer feedback, this is a heavy undertaking by a brand. It means a sizable $ commitment as well as the team involved. This is something that can only be achieved by an executive sponsoring it, and the significant undertaking is justified by serving the insights to as many stakeholders as possible.

Because it’s one system that caters to a wide range of roles, and because it is maintained by a shared group of experts, it is geared for the lowest common denominator in terms of insights: 

  • Price/Value 
  • Quality
  • Loyalty
  • ….

This type of data doesn’t lend itself to quick decision making. If for example someone within the brand in an operational role wants to take a single product from $X revenue to $2X revenue the insights available are not detailed enough to do so.

For these details you need deep understanding of what to fix/change/position/market with your product. How are you going to get it from one centralized system that caters to all the products in the organization PLUS the brand level insights? The short answer is you won’t. The typical solution would be to augment the available high-level insights with deep research through consultants, internal efforts or a collection of surveys.

If you don’t have the money or resources for additional research, you can try to go back to this centralized group, stand in line so they can consider adding your needs for custom insights into their to-do list, and perhaps down the road you will get the additional reports.

If we sum it all up, the enterprise CX solutions are best serving the executives within brands with high level views of sentiment and brand perception, but they leave the masses within brands that need detailed actionable insights for their operational role without quality insights. 

This means that operational, mid management roles, such as a product manager, marketing manager, customer service manager, insights manager etc. – will need to spend extra time and money to go after the details they need.

What are the makings of a DIY CX Analytics solution?

Realizing that one-size-fits-all solutions can’t satisfy the dynamic needs of thousands of operational roles in brands, the fundamentals of a personal, Do It Yourself (DIY) analytics solution should allow these individuals to be:

  • Self sufficient
    1. Usable by any business user
    2. Can be purchased by one individual (No IT or Experts involved)
  • Getting timely and actionable insights
    1. Delivering actionable insights at the product (SKU) level
    2. Short time to insights (Days vs months)
  • Tweaking the insights at will, using a wide range of filters
  • Priced for an individual or small department 

With these characteristics let’s take another example – of a new product launch.

According to Harvard Business School 95 percent of new consumer products fail. In a typical scenario you launched your product and backed it up with marketing hype, coupons and free samples. First few months are great and feedback is in sync with the hype you generated. Then sales start to decline and in the typical scenario and an enterprise CX analytics solution you’re not sure why. With the DIY personal CX version, all you have to do is log in and analyze the trends and sentiments around the new SKU to know what is going on and how to address it. This is exactly what should save you months and months of surveys and expensive ad-hoc research…

Conclusion

Most CX Analytics solutions today are centralized systems that are incapable of keeping up with the pace of modern consumers as well as the needs of different mid to low level roles within brands. The reliance on human experts and IT in the loop limit the competitiveness of brands due to

  • Time to insights
  • Insights not actionable for different specific roles and tasks

There are 2 key factors that drive the need for a faster moving brand and one that can empower quality decision making across all levels of employees:

  • Consumers become more educated, have more eCommerce options, a wider range of offerings and new brands and products coming in at regular flow
  • Brands become more sophisticated with a wider range of operational roles like product, sales, marketing, service, eCommerce etc. that each need different quality data

This is why you will see a shift from the enterprise CX analytics solutions into DIY analytics solutions that will cater better to the masses within brands. Revuze addresses just this with the first personal, low touch solution that can mine consumer insights automatically at the single SKU level as well as at the entire market level. Reach out to us to find out more here.

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