Internal data

The 4 things brands can learn by mining internal data

In their efforts to understand their customers and consumers in general brands often focus on mining hot sources such as social media but often neglect mining internal data piling up on their servers.

 

Typically, brands collect customer/consumer opinions from various sources:

 

  • Call center
  • Website forms (and sometime reviews)
  • Open ended survey questions
  • Chat
  • Emails
  • Stores (For retailers)

 

These sources can pile up to a good amount of data, and it’s a shame not to leverage it as its likely much larger opinions amount then a typical survey or focus group, not to mention much more diversified and detailed.

 

In addition, these sources mostly represent the brand existing customers and not just the general population, which means that the insights this data contains can really give us clarity on what is important to customers, not just potential customers.

 

  1. Brand feedback

First and foremost, this data helps us better understand the brand perception. If possible, you can even split the data to existing and potential customers to understand the brand perception by these different populations. In this category brands will learn generic aspects about how they are perceived, for example:

  • Overall satisfaction
  • Price/value for money
  • Loyalty

 

  1. Customer service/satisfaction

Since a lot of these sources (stores, call center, emails, surveys…) are targeted at your existing customers, this is exactly where they are sharing their thoughts about you and your offerings.

Specifically, for customer service you can learn about the main likes and disliked of your customers. What areas of the service they are happy/unhappy about as well as what they like and don’t like about your offerings, where you can improve.

Example topics here include:

  • Post purchase service
  • Product returns/modifications
  • Repairs and maintenance

 

  1. Product/Service experience

Similar to customer service/satisfaction, here there’s an opportunity to learn directly from customers and customers to be about their likes and dislikes on the brand product and/or services. This is a great opportunity to evolve the offerings, innovate, learn how to better position them etc. Example topics here that are common to a wide range of consumer goods or services include:

  • Ease of use
  • Packaging
  • Quality

 

  1. Purchase experience

This is yet another great opportunity to learn directly from customers about their experience in purchasing your offerings, what they liked and disliked. With these insights your goal will be to improve the purchase experience and impact your supply chain decisions. Example topics here that are common to a wide range of consumer goods or services include:

  • Business hours
  • Item availability
  • Shipping

 

Conclusion

With internal data captured about your customers and potential customers there’s great opportunity to leverage a volume of opinions on a variety of topics to better understand your audience and the areas you can improve on. These areas can span marketing, product, customer service, innovation and competitive analysis. All you need to do is just mine this data and leverage the results.

Revuze is an innovative technology vendor that addresses just this with the first self service customer analytics solution. Without any involvement from IT, Revuze typically delivers 40-60 topics of interest hiding in your data, and it does it in a self-service solution that anyone can use.

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Warning signs 1

New white paper – 3 signs you need a self-service consumer insights solution

There’s a new Revuze white paper in town! If you’re role requires deep understanding of consumers and/or competitors, then you should look for these 3 early warning signs that you’re behind on your consumer insights. Below are some of the white paper highlights, and to read the full report just contact us. Enjoy:

 

In this paper we cover the warning signs operational roles within brands need to pay attention to if they rely on deep understanding of consumers and market for the role. Once you hit any of these signs we highly recommend that you adopt a self-service consumer insights solution so that you can move forward faster while making better data driven decision.

 

In the report we cover the following:

  • State of the consumer insights industry
  • The 3 early warning signs that you should care about when going after consumer insights
  • How self-service consumer insights solutions should address these signs

 

To read the full paper please fill the form at http://revuze.it/contact/

 

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Source icon

The top 3 sources for customer analytics

According to a recent report by IBM’s Marketing Cloud, 90% of the world’s data was created in the last 2 years!!! It means the world had grown 10X in data at a rate of 2.5 quintillion bytes of data a day! There are so many implications for this – where do you save all this data, how do you manage it, is there such a thing as too much data? What does it mean for the future? Will we have 10X data growth again in 2 years?

With so much data out there, its an opportunity to upgrade your customer analytics initiatives!

Customer analytics used to be about data sampling, surveys and focus groups and in general about estimating the overall market behavior and preferences based on a small group of individuals. Now we vast amounts of data you can actually analyze loads of opinions and not need a small sample.
But where are the best data sources for that?

Online reviews on brand and eCommerce sites

According to a recent research 97 percent of customers said they had read reviews in 2017. There are so many ways now for consumers to share data and information that online consumer reviews and feedback data is in fact become the world largest consumer panel. And because it is an anonymous one it’s easy to share loads and loads of data since no one is worried about saying the wrong thing.

In fact, its even better then just the world largest consumer panel, as consumers are not concerned that they are listened to and as such convey their opinions more freely…

This data source in our experience seems to be covering a wide range of areas. Examples are – customer service and QA (returns, complaints, failures, missing parts, packaging), product or service (popular features, negative reviews, competitive analysis) and market research (analyzing brands, products and sentiment and looking for white spaces for innovation).

Another benefit is that it is the most detailed in terms of industry coverage – it will cover the most brands in your industry and will also cover most SKUs.

Internal data – from stores, call center, open ended surveys or even emails

This is the real hidden gem. On your own servers you’ve to the most direct and brand specific feedback you can get. Your own customers are expressing their thoughts about you and your offerings, now all you have to do is pick it up.

Typical data sources include the call center, open ended surveys and emails. If you’re in retail, data from the stores around customer feedback or customer returns or even complaints can serve you well if you drill down on it.

The point is this is data about your brand, products and/or services and nothing else. Use it wisely.

Social media

Given that this year (2018), an estimated 3.2 billion people will be using social media worldwide there is no shortage of data to mine in social media. This is why you should make the most of it. Keep in mind several things that make it typically the least detailed oriented data source (in our experience):

• There’s lots of “noise” in social media, so you need solutions that can clean up the data easily
• Feedback is typically delivered at the brand level, you wont see mentions of SKUs
Still from a sheer volume perspective this is likely the source with the highest volume of feedback you can get today.

Conclusion

On a positive note we now have more data we ever dreamt about to better analyze our customers. Further to that its diversified as we can get it from industry sites like retailers or other brands, we can get it from our own call center or social media outlets and in each source the opinions and focus will be slightly different. So we will get a great 360 degree view of our industry if we can mine these opinions accurately and with high granularity.

Revuze is an innovative technology vendor that addresses just this with the first self service customer analytics solution. Without any involvement from your IT or Insights team, quick to setup and intuitive for use by anyone, Revuze typically delivers 5-10X the granularity of topics compared to anything else, and it does it without humans, in a self-training solution that can adapt to the moving target of consumer interests in the fast pace of today’s data growth rates

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Self Service

Announcing self-service consumer intelligence

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

Introducing Revuze

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.

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Sentiments

The top 3 things that are broken in Sentiment Analysis

With Sentiment Analysis brands and retailers aim to mine the opinions of consumers to be able to understand how they are performing across a number of topics as well as how their competitors are performing along the same topics. Given that this year (2018), an estimated 3.2 billion people will be using social media worldwide there is no shortage of data to mine in social media, and there are additional data sources such as online reviews, call center data, survey results, etc.

The challenge with all this data is that it’s very hard to mine it. It’s a lot of data, and mining it relies on having a team of experts around your Sentiment Analysis software to look for keywords and terms patterns. This is a lot of effort as you need to guess and test quite a bit:

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 where does this leave us? It leaves us in a world where Sentiment Analysis is relying on lengthy, ongoing substantial manual effort to deliver visibility into a short list of topics that are typically high-level ones.

Where would we want to be if we could imagine an optimal world?

1 – Automation

With so much information 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

Conclusion

Sentiment Analysis is widely used today as a way to understand consumers, the challenge with the current state of the industry is that it is –

• Requiring lots of manual efforts on the journey to value
• Consumer understanding is limited to parts of the topics consumers care about
• The end result is only useable/accessible to a small subset of roles in the organization

The key challenge is to drive meaningful insights from the masses of data that you have, and for this, most existing technologies that rely on humans are not strong enough. Consumer interests are too much of a fast-moving target that is also very eclectic in terms of patterns.

The good news is that the data is there for us to see and mine.

Revuze is an innovative software vendor that addresses just this with the first self training, low 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.

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iiex

Free IIEX presentations available, including our Procter & Gamble session

Revuze was honored to speak at IIeX Atlanta by invitation from Procter & Gamble for their IIEX session. Just now IIEX made all the event presentation available for the wide audience. See https://iiex-na.insightinnovation.org/page/1302912/past-presentations . The Revuze presentation is titled “Making Better Decisions Faster, by Turning Every Employee Into an Analyst” and is showing at the 15:00 time slot.

As a reminder Revuze is the first automated market research technology, turning unstructured text from eCommerce sites, Social Media, call centers, emails, surveys etc. into a complete market research analysis, covering brands to products to features, without any involvement of IT or data scientists or experts.

With our unique technology any employee of Procter & Gamble that needs deep market information to make daily business decisions is empowered to do so with high level of confidence and without the need for lengthy cycles of research, analysts or IT projects.

Our session at IIEX covered the recent drivers that require brands to empower a wide range of employees to make better data driven decisions. This includes:

• 90% of the world data was created in the last 2 years per IBM
• Most of the answers brands look for are already there
• Mining the data today required experts, IT and delivers partial (<20%) results • Why brands need to empower a wide range of employees to make faster and better decisions • Sample use benefits across Product, Marketing, Research & QA • How Revuze delivers 5-10X more granularity to brands so they can make well educated decisions As a reminder when we came back from IIEX we shared the 3 Insights from IIEX Atlanta – go and catch up on the latest trends we report there.

See you in our next post!

The Revuze team.

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Business change

How to identify new, disruptive brands in the consumer space?

The business world today is moving at a much faster pace, and new incumbents are rising quickly, disrupting well established industries with new technologies or new business models. Dollar Shave Club is a great example for a new business model and Uber represents well the new technologies available today and how they can turn around an existing industry.

In this kind of an era consumer brands are more concerned than ever with looking at the rear-view mirror and identifying fast coming disruptors. Unfortunately, it’s extremely, extremely difficult.

Difficulties in identifying disrupting brands

When you sum it up the biggest challenge is that you don’t know who the new brands are till it’s way too late. But let’s break it down to 4 parts:

• Looking for something you don’t know

As these are new brands, by definition, you don’t know that they exist and definitely don’t know their name. So how can you look for them?
The answer lies in looking at all new brands in the market, but that by itself also presents a challenge – it’s a lot of work, tracking every brand out there, monitoring progress etc.

• Where do you look for it?

Since young brands tend to be more modern and more cost effective the immediate place to look is online. eCommerce sites are a good start. Reviews websites as well. Its just that there’s a lot of them. You can also track specific competitor sites if you believe they are about to launch disruptive innovations

• Who’s looking for it?

Who’s responsible for looking out for them? Is it a centralized team analyzing it all or do we distribute the responsibility to every customer facing role? Are they measured on this?

• How do you know it’s a threat?

Once you track new brands, how do you decide when they become a threat? What are you tracking? Growth? Sentiment? Trends? How do you validate these?

What are the ways to identify disrupting brands?

Looking at the same 4 areas:

• Looking for something you don’t know

When you don’t know what to look for, you need to look at everything. You can scan all the product categories in your industry. Even done manually you can go to Amazon once a month and look at the hot products under your industry.

• Where do you look for it?

As we suggested before, the major eCommerce sites are a good place start. Reviews websites as well if they are relevant for your industry. Scanning industry publications for innovation news can’t hurt either, as well as tracking specific competitor sites, especially the ones you believe are the most innovative.

• Who’s looking for it?

You can either go with a centralized team responsible for tracking industry innovations or you can distribute the responsibility to a wide range of customer/market facing roles.
Centralized team responsibility advantage is that you know who owns this. Risk is that a small team will not be able to cover much. On the other hand distributing the responsibility to a wider range of people guarantees more eyes on the market and likely also more detailed review as having a centralized team include experts on an entire industry is rare.

• How do you know it’s a threat?

Understanding the threat level of private new incumbents is challenging. These are not public companies so you can’t monitor revenue growth from public filings. The ideal is to track consumer Sentiment for the new brand’s products and how it trends up on a regular basis. You can also track how these products perform against the industry average in terms of star ratings, and sentiment to understand their level of superiority.

Conclusion

The battle for the modern consumer is already taking place, and agile new incumbents are the ones that threaten existing industries. A key aspect to fighting back is to empower your front liners and 2nd liners with accurate, granular market insights. With a small army of market experts that have all the data they need with granular details that they can slice and dice at will you can identify threats and identify them early.

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iiex

3 Insights from IIEX Atlanta

Considered to be one of the most innovative consumer insights events in the world, IIEX North America in Atlanta just now catered to over 1,000 consumer insight and market research experts. As speakers and attendants to the event the Revuze team got a good run down of the tracks, vendors and overall vibe and wanted to share the top 3 insights with you all.

1. Market research is evolving VERY slowly

With lots of pockets of innovation, market research tries to move forward and have researchers better understand consumers across a number of areas, however it seems its mostly small steps forward and not leaps and bounds.
Some highlights of innovation areas were:

• Understanding emotions and behaviors
• Video insights
• Share economy for cost reduction

However, in parallel, a lot of the market research basic questions were left open. Several panels and lectures discussed concept testing, higher precision in research and ways to automate the lengthy research process. To us it felt like the fundamental issues were still in place – how do you know what to ask, who to ask, and do it in a timely manner and with good enough statistical coverage. More of the same. What does it matter if you can add a little more data into your research from video or emotions if you’re still not sure your asking or answering the right questions that are on top of your audience mind and if you can’t do this quickly and with data that is granular enough to take action on?

2. Hype cycles – AI down, Blockchain up

While AI (Artificial Intelligence) is still going strong and was well represented, it seems the hype cycle for it was scaling down. Blockchain on the other end was on the rise with some prominent lectures and panels. While there were no demonstrations of specific applications for Blockchain in research or insights enthusiasm was high but not translated into actual offerings yet, so still a bit premature

3. Research is still heavily reliant on humans

With so many of the speakers, panelists, attendants and exhibitors being with research companies or research consultants, it felt like the overall theme is that this is a market that is still hung on manual labor, intuition and talent. We at Revuze believe the future is with automated market research that humans can leverage to make business decisions off. It seems like the market is way behind on this one.

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Mainframe

Why the consumer insights space is still in the mainframe era?

Consumer insights are not accessible to the wide audience within organizations. You may think they are, but they’re not. What is available in brands is a centralized system that circulates predefined set of insights to different audiences. And because its one system that caters to a wide range of roles, and because its setup by people with limited time, it is set for the lowest common denominator in terms of insights:

• Value for money
• Loyalty
• Quality
• ….

One size fits all doesn’t really cut it anymore. Let’s say you work for one of the brands as a product manager or product researcher. You are responsible for one product or a group of products. In order to move the needle and take your product from $X revenue to $2X revenue you need a detailed plan of what to do. For the details you need deep understanding of what to fix/change/position/market with your product/s. How are you going to get it from one centralized system that caters to all the products in the organization PLUS the brand? Short answer is you won’t.

This is the mainframe side of the story. One system or a few systems that are managed by IT or other centralized groups and is/are supposed to track and deliver granular insights to all products of the brand.

If as the product manager you need new insights as your product or competitors now have new capabilities you need to go and stand in line for the central groups to add these insights into the system so hopefully down the road you will get the right reports.
I think by now you got the picture:

• It’s slow
• It’s not granular
• It’s not personalized

It’s not that these systems are useless, it’s just that they mostly cater to the executives in the brand and is less valuable to the front line in the brand that needs to make daily, data driven decisions about specific aspects of a specific product – is the handle of the razor convenient? Are my paper towels as soft or as durable as my competition? Is my facial cream leaving residue and can I fix it?

The business world today is moving at a much faster pace, and rapid changes in competition, demand, technology etc. have made it more critical than ever for consumer brands to be able to respond to changes quickly. But according to a recent McKinsey Survey, organizational agility brands are looking to apply agile ways of working to areas that are customer focused such as innovation, customer experience, sales and servicing, and product management.

The common theme to better customer focused operations is accurate data and insights on the consumers. There is so much data out there today so there is no shortage of it but mining it and making it available easily to a wide range of people who need it within the business is still challenging for most. And this is where there is a need for the PC (Personal computer) version of Consumer Insights.

What’s the PC side of Consumer Insights?

We need a PC (or tablet/smartphone…) because we each like and need different things. And we want quick access to them. Similarly in brands you’d expect any business decision taken to be backed by the specific data and insights needed – whether you’re deciding on a marketing campaign against a specific competitor or deciding on your next product launch. Basically the PC version of Consumer Insights needs to provide:

• Granular data for the specific role about the product (or service) and competing products (or services)
• Real time access
• Flexible reporting so I can build my own

With this every employee in the brand that needs to make business decisions can win the war. He/She knows how to set priorities and where to focus.

Conclusion

A consumer-focused product or a service can fail on one specific factor (See recent example of Samsung Note 7). However to succeed with a consumer product/service you need to know all of the dozens of aspects that your consumers care about in your offering. Only if you get them all (or mostly all) right you will get more stars, revenue and positive sentiment. However according to Harvard Business School about 95 percent of new consumer products fail. As a brand your chance to succeed is to give your employees PCs and not send them back to the mainframe to stand in line. Only this way they will get the insights they need to win the war.

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Brand list

The 50 things consumers care about in your brand

There’s a huge misconception within consumer brands. There is a belief that consumers care about 5-10 different aspects of a product of a service or a brand. Even if you search online for “top factors consumers consider in a product” you find a long array of “5 factors” or “top factors” or even “top 10 factors” articles. Reality is consumers are much savvier today and typically consider 40-60 different aspects of a product or a service as part of their experience. Even if a specific consumer cares about just 10-20 aspects, they are not necessarily the same ones as other consumers, leading to the fact that as a group, consumers care about many aspects of your products or services. So, when you’re reading another “top 5” or “top 10” article, you’re actually leaving behind 30-50 different things your customers care about when considering your brand!

Where consumers spill their hearts?

To identify consumer opinions you should look at getting data from either external or internal sources. External ones are eCommerce sites, brand sites or social media. Internal data sources can be your call center, customer service email, store data and even open ended survey questions etc.

Common themes

The common themes that consumers care about when considering goods and services are repetitive and are typically along the lines of:

• Price
• Value for money
• Quality
• Loyalty (existing customers)
• Ratings and reviews
• Free shipping
• Availability
• Samples and promotions
• A friend recommended
• ….

Even with this sample list we’re almost at 10 topics, and these are not product specific, imagine how many things people can care about around a specific product.

Specific examples

Let’s pick a couple of industries as an example to look at what people care about when taking an interest in a product or a service. For these the data came from public online sources for ratings and reviews in the United States.

In the paper care industry, spanning paper towels, toilet paper etc. consumers care about the following 41 topics as part of their buying and usage experience:

1. Overall Satisfaction
2. Price/Value For Money
3. Softness
4. Loyalty
5. Durability
6. Cleanliness
7. Size
8. Economical
9. Quality
10. Tensile Strength
11. Absorbance
12. Thickness
13. Texture
14. Functionality
15. Is It Recommended?
16. Returning Customer
17. Free Samples & Coupons
18. Damaged
19. Looks & Design
20. Ease Of Use
21. Perforations
22. Packaging
23. Features
24. Residue
25. Flushable
26. Smell
27. Meets Expectations
28. Cardboard Tubes
29. Time/Frequency Of Use
30. Cold & Allergy
31. Lint
32. Comfortable
33. Skin Sensitivity
34. Shipping
35. Item Availability
36. Efficiency
37. Convenience
38. Environmental
39. Moisture
40. Ingredients
41. Fragrance Sensitivity

In the smartphones industry (based on 2016 data, which is a bit dated but gives you the overall sense) consumers care about the following 56 topics as part of their buying and usage experience:

1. Overall Satisfaction
2. Price/Value For Money
3. Battery
4. Stability
5. Meets Expectations
6. Screen
7. Memory & Storage
8. Camera & Pictures
9. Size & Weight
10. Speed
11. Operating System
12. Looks & Design
13. Is It Recommended?
14. Upgrade
15. Video
16. Applications
17. Return & Refund
18. Phone Plan
19. Sound
20. Features
21. Shipping
22. Phone Calls
23. Amazon
24. Returning Customer
25. Unlocked Phone
26. Sim Card
27. Seller
28. Case & Screen Protector
29. Star Rating
30. Quality
31. Convenience
32. Activation
33. Hardware
34. Internet Access
35. Performance
36. Setup
37. Ease Of Use
38. Security
39. Customer Service
40. Games
41. Text Message
42. Learning Curve
43. Keyboard
44. Headset
45. Phone Level
46. Music
47. Waterproof
48. Touch Screen
49. Navigation/Gps
50. Lights
51. Usb
52. Cable
53. Transfer Data
54. Apps Store
55. Gift Card
56. Projection Feature

These are just 2 examples from different ends of the market – sophisticated products vs simple ones. In both cases you see consumers showing interest in 40-60 different topics in the market.

Conclusion

A consumer-focused product or a service can fail on one specific factor, see the not too recent example of Samsung Note 7. According to Harvard Business School about 95 percent of new consumer products fail. Harvard Business School encourages consumer brands to look at products the way customers do: as a way to get a job done. What you should be interested in is identifying the 40-60 factors that your consumers are considering when they are looking at what you offer. You really need to know all of them as a way to make sure you identify if you are failing on one or more of them.

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