Understanding consumers is a difficult task. It is typically a lengthy process, expensive (involving experts) and resulting in partial understanding. Till recently, solutions in the market required your IT experts and data scientists to work with the vendors’ experts to configure their products and integrate them with internal systems. Because of this the pricing dynamics were of prices and relied on high-touch salesmanship, and in addition the implementation, customization and integrations often incurred additional costs.
Due to the centralized nature of such a project, involving executives and experts, once a traditional Consumer Insights system was up and running, stakeholders had to wait for weekly or monthly reports, meaning longer decisions cycles, and longer time to validate the decisions with market data. With this type of a centralized service organization, changing or adding a report required a request to IT and a long wait time to implementation, in a successful scenario.
If we sum if all up, it leads us to the following State of the Industry:
• Consumer insights are not easily accessible to the wide audience within organizations. Being centralized means you get predefined reports and any change (if possible) will take time
• Since there is one system that caters to a wide range of roles, and because it’s setup by people with limited time, it is set for the lowest common denominator in terms of insights, meaning generic topics – Price, Value, Loyalty, etc.
• The above leaves operational roles in the organization without granular data and with long decision cycles
This is why like lots of other industries, there’s a strong need for self service consumer intelligence, so individuals in business roles within brands can enjoy –
• Faster, data driven decisions
• Without relying on experts or centrally controlled systems
• With granular, product/service specific data
What makes self-service consumer insights tick
The challenge with mining modern consumer insights is that it’s a lot of data, and mining it with human experts requires a lot of guessing and testing:
1. Guess all the topics that consumers care about around a product or a service. How can you guess ALL the different topics consumers talk about? The short answer is you can’t, which means you will miss quite a bit. This is why in most cases these teams focus on a short list of high level topics – value for money, loyalty, quality, etc.
2. Guess ALL the different ways that people talk about the same topic: Imagine how kids, teenagers, boys, girls, adults of different ages all speak about the same topic. If we take the example of an electrical appliance battery, you’d need to look for all negative expressions about it such as “doesn’t hold charge” or “weak battery” or “dies on me after 2 hours of use”
3. Guess new hot topic: With new products comes new issues. You’d need to know something has come up in order for you to look for it, and then obviously you need to guess the different variations of terms used by to describe it
So how would self service address these challenges?
1 – Automation
With so much data available online and in house, solutions relying on humans for pattern identification, even AI driven systems that rely on IT and experts to setup, are too slow. IT and data scientists will just not be able to respond quickly enough to every business data request while in parallel they still need to fine tune and configure a data mining system to respond to competitor and market changes. The key is automation. Find the automated data mining systems that can harvest the insights without delays. Fortunately, they exist now.
2 – Granularity
If you figured out a way to access insights and mine data automatically, you need to keep in mind that generic, one size fits all data (loyalty, quality…) is not usable to all roles in the organization. Specific roles, operational roles, need specific data. A Product Manager needs granular data on the product that he is selling, not on the category or the brand. Therefore you need granular data that each role can slice and dice for their own use and needs. Also keep in mind operation roles needs change ongoing, one day it’s a product competitive analysis and the next day its positioning or roadmap. They all can benefit from granular data, but different compositions of it for the different tasks.
3 – Accessibility
Once we have granular data we can get automatically, you’d want to encourage a wide use of this data across roles in the organization. Unlike the current status where centralized groups maintain the Sentiment Analysis software, the optimal solution needs to be one that everyone can use. It needs to be intuitive, and autonomous. If the solution is complex or if it requires IT or Insights or any other centralized group to change or support or configure. You want to empower the masses to take action and they can’t take it if they don’t have control
Mining meaningful insights from the masses of data that brands have is challenging. All the existing technologies that rely on humans are not fast/granular/affordable enough.
The good news is that Revuze is here and is offering the first self-service consumer intelligence solution.
Revuze built the first self training, no touch solution that can mine consumer data automatically. This is why it’s much more granular and typically delivers 5-10X the insights compared to anything else, and it does it without humans helping.