Want to learn more about generating and utilizing consumer insights? You’re in the right place. In this guide, we walk you through all you need to know about turning consumer insights into actionable results, including:
- Making sense of your data
- What are actionable insights?
- Collecting feedback to generate consumer insights
- Analyzing consumer insights to generate actionable results
- Case study: How Boots is leveraging its consumer insights
Making sense of your data
Once you’ve got the right systems in place, you’ll generate immense amounts of data round the clock. Wal-Mart, for instance, generates about 2.5 petabytes of data from its customers’ transactions on an hourly basis. 1 petabyte is one quadrillion bytes, or 1 thousand billion bytes — basically, a mind-boggling amount of data.Bearing this in mind, it doesn’t make sense to just sit on your data, instead of using it to inform your key decision-making. Once you start to delve into your data and analyze it, you’ll find that it can allow you to answer important questions that will help you evolve and level up your business strategy, including:
- Which customers are the most profitable to serve? Why?
- What’s the marketing channel that generates the most profitable leads?
- Which existing markets are the most profitable to serve? Which new markets are likely to be profitable, and worth entering?
- What prices are optimal, and would help to maximize the company’s profits?
- What are the bottlenecks holding the company back?
- What’s the best time to launch a new product or product line?
In a nutshell, data and consumer insights affect every part of your business — including marketing, sales, operations, finance, and more. At the end of the day, tapping on your data is the best way to achieve your business goals, and grow your company.
Turning data into actionable results
If you’re not 100% sure about the difference between data and actionable results (or insights), the latter is basically what you get once you analyze your data.To unpack this more: data refers to raw and unprocessed information; this includes both numerical information (quantitative data) and text (qualitative data). Obviously, it would be extremely tedious and mind-numbing to sift through pages and pages of data in order to analyze the information and try and generate insights. Bearing this in mind, data scientists first process and organize their data (think dashboards and visualizations), then analyze them to come up with actionable results.
Now, let’s turn our attention to the term “actionable results”. This term is fairly self-explanatory — it refers to results or insights that companies can directly act upon.
For example, the statement “Free shipping is an important factor for customers shopping online” is not an actionable insight. Firstly, this insight is rather obvious — pretty much every eCommerce store owner knows that free shipping can influence a customer’s purchasing decision to a large degree; secondly, the insight doesn’t lend itself to any sort of decision-making on a manager or business owner’s part.
Here’s another example: if a company generates an insight that states “When consumers interact with an eCommerce store via three different touch points before making their first purchase, this results in a 200% increase in Average Order Value (AOV)”, this does fall under the category of actionable insights. Firstly, the statement isn’t common knowledge, so it counts as an “insight”. Also, because eCommerce store owners can act upon it (by reaching out to customers via more touch points), it’s also actionable.
Collecting feedback to generate consumer insights
Back in the day, companies who wanted to collect customer feedback or measure customer satisfaction would simply craft a survey, and send it out to their entire database. These days, however, there’s a lot more finesse involved in the way businesses capture customer data.First and foremost, companies are putting more thought into when they collect customer data. In the past, as mentioned, you’d just send out a survey without thinking too much about it. But companies today don’t just want to capture customer sentiment at a specific point (when they send out their survey), they want to understand their users’ satisfaction levels throughout the entire customer journey, and at each touchpoint.
On top of that, companies used to rely exclusively on surveys to generate customer feedback, but they’re now working towards a more holistic approach that provides them with a 360-degree view. Simply put, surveys are now seen as the most “basic” thing a company can do to gather customer feedback; on top of that, companies are also engaging in media monitoring, reading product reviews, and other activities in order to be more attuned to their customers.
Collecting feedback via surveys
While surveys are no longer the only strategy or tactic that customer insight teams rely on, they’re nevertheless an important source of customer feedback. If you’ve got a hypothesis that you want to shed some light on, there’s no better way to validate or test out that hypothesis than to craft a survey and invite your customers to speak for themselves.If you want to create a survey to generate customer feedback, keep these tips in mind:
Keep the survey short
The shorter your survey is, the higher your survey completion rate will be — simple as that. Make sure you also mention that your survey is short when you’re emailing or contacting your customers about said survey.
For instance, add a one-liner to your email that says: PS: This survey comprises of just five questions, and will only take a minute of your time!
Provide an incentive
Want to generate more responses, fast? One easy way to do that is to provide an incentive for respondents to complete the survey. If you run an eCommerce store, for example, you can provide your customers with free shipping or 10% off their next order upon completion of the survey.
Ask questions independently
Don’t group questions together, regardless of how similar and interconnected you think they are. Doing this will skew your results, and make them less accurate.
Here’s a bad example:
- Would you be more likely to purchase a product if an eCommerce store offers free shipping and returns?
Here’s a good example:
- Q1: Would you be more likely to purchase a product if an eCommerce store offers free shipping?
- Q2: Would you be more likely to purchase a product if an eCommerce store offers free returns?
Provide an “Others” option
For multiple-choice questions, always provide an “Others” option that your respondents can choose if they feel like none of the other options illustrate their preferences. For example:
Q: What is your biggest deal-breaker when you’re shopping online?
- Option 1: Slow shipping times
- Option 2: Expensive shipping fee
- Option 3: No returns allowed
- Option 4: Expensive product prices
- Option 5: Poor product range
If you don’t offer an “Option 6: Others” in the above scenario, you’re not catering to consumers who find that none of the five options are relevant to them. For example, some consumers might dislike when eCommerce stores don’t feature any product reviews, or when the currencies displayed aren’t localized, or when websites take forever to load.
By not providing an “Others” option and allowing these respondents to elaborate, you’re missing out on a ton of insightful data.
Collecting feedback via other sources
While companies still use surveys to collect customer feedback, they also supplement their efforts by generating feedback and key insights from other sources. This provides companies with a more well-rounded view, and allows them to become more customer-centric (as opposed to product-centric).
Firstly, many companies are now engaging in media monitoring to track instances when their brand is mentioned on social media. As Caroline Wells, Head of Customer Experience and Insight at Financial Ombudsman Service points out, the interesting thing is that people still don’t tend to complain directly to a business through social media, but they will post a comment online and complain about a company. As Caroline mentions, consumers expect businesses to pick up this negative feedback and do something with it, even if they aren’t directly tagging a company or posting on the company’s social media page.
Social media aside, another key source of customer feedback is product reviews. Now, if you’re selling products on your own standalone site, you’ll probably have to come up with some sort of review program or scheme to encourage customers to review your products. But if you’re selling on a marketplace such as Amazon, chances are you’ll have already amassed a treasure trove of reviews, without lifting a finger.
These reviews cover a wide range of topics, including:
- Why the customer chose to purchase your product
- Which products your customer was looking at (before deciding to buy yours)
- The features of your product that your customer uses (and the features that they don’t)
- What they like and dislike about your product
- What they use your product for
- When they use your product
- How they think your product can be improved
Analyzing consumer insights to generate actionable results
Now that you have all your data on hand, the next step is to analyze the data to generate actionable results.
Traditionally, you’d employ a data scientist (or a team of scientists) to clean and compile your data, then analyze it and generate results from it. Now, the problem with doing this is that most companies have far too much data to be processing and analyzing their data manually. For example, say you have a datasheet that contains 10 key metrics and 20 dimensions. If you want to slice and dice a single metric across 20 different dimensions, that would require your scientists to run 20 different analyses. If you want to combine different dimensions to conduct multi-dimensional analyses (eg analyzing by age and gender), the number of analyses that you’re looking at will increase exponentially.
Bearing this in mind, relying on the old-school, manual method of analyzing data simply isn’t feasible. Thankfully, there is another option for companies who want to make the most of their data (without having to spend a fortune on data scientist teams) — and that’s augmented analytics tools such as Wonderflow.
Simply put, Wonderflow is an integrated enterprise solution that helps companies turn the voice of the customer into actions. The tool utilizes machine learning and Natural Language Processing (NLP) technology to process and analyze your data, and churns out actionable results that you can incorporate into your business strategy.
Analyzing multi-language customer feedback in large volume from different sources is a complex process. With an AI-based technology and years of experience, Wonderflow is helping global brands to become customer-centric. Find out more about our solution.
Because Wonderflow is engineered with NLP technology, you can actually pose specific questions to the tool, and it’ll pull up relevant data to answer your questions. A sales manager might ask “Why was sales down in the last quarter?”, while a marketing manager might ask “Should we launch our new PPC campaign this month or next month?”
Case study: How Boots is leveraging its consumer insights
Boots is the UK’s leading health and beauty retailer; it was founded back in 1849, and it currently operates over 2,500 stores across the UK. The company wanted to encourage its loyalty card members (15 million and counting!) to increase their spend so that it could grow its revenue, and it successfully leveraged its consumer insights to do just that. Martin Squires, Head of Customer Insight at Boots UK, says that the company knew that the way forward was to harness its data to deliver compelling, timely and personalised offers. As Squires puts it, Boots would have to “extract actionable insights” from their “massive volumes of transaction data” to achieve their goal.
One problem standing in the way? Boots’ previous approach to analytics relied heavily on manual processes, and their analysts actually spent around 80% of their time collating and preparing data from six separate source systems.
As Squires explains, these time-consuming processes, combined with a reliance on legacy analytics tools, made it extremely difficult to create a single view of each customer. As a result, Boots was forced to use generic, catch-all marketing campaigns, such as issuing a coupon for the same product to every customer when their basket value was above a certain threshold. To tackle this problem, Boots embarked on a colossal project to integrate its different channels. Today, the company uses their database software as a central data warehouse repository for its sales transaction data, which means it can match transactions to individual loyalty card customers, and use each person’s unique purchasing histories and preferences to predict the next-best actions.
Obviously, this puts Boots in a good place to execute highly targeted marketing campaigns; it also lets them personalize their offers and messaging to their different customers. Squires says that Boots now launches 70% more tailored messages every year, and that the company has seen a “dramatic increase” in incremental sales from the Boots Advantage Card customers, enabling them to strengthen their position in a competitive market.