Yearly, 30,000 new consumer products are launched.

And 95 percent fail.

Why?

Businesses over-invest in innovation and under-invest in opportunities to engage with their customers repeatedly overtime to uncover the first-party data they need.

The brightest minds are coming up with amazing methodologies, but they continue to fail.

Why?

Because the output is only as good as the input. Ideally, from known-users who can provide agile, first-party data that can be used to augment and build-upon existing user metrics and behavioral data.

Regardless of the method, untested hypotheses and surrounding decisions that are driven by conjecture, opinion and slivers of data lead to poor outcomes. You can change the method as much as you want, but the crux of the problem is the data that drives those decisions must be agile, accurate and sourced from people who actually use your products.

The rise of agile product development

In the face of ongoing disruption and rapidly changing customer expectations, businesses are constantly looking for opportunities to develop a competitive advantage. That need has given rise to agile product development teams. Their approach, in concept, is to uncover and understand a problem or opportunity and then build a minimum viable product that addresses that problem in as little time as possible.

The process is to build and test features in an iterative manner. Each iteration delivers new capability or functionality until the team has built a minimum viable product. Generally, that process plays out in rolling, two-week sprint cycles.

The crux of agile product development is the need for specific and timely customer feedback at every stage of the innovation cycle – feedback that complements the cadence of those sprint cycles and builds upon existing user metrics. Short feedback loops enable teams to leverage what they now in order to learn faster and iterate while the development costs (and the risk of failure) are low.

“It’s very important to have a feedback loop, where you’re constantly thinking about what you’ve done and how you could be doing it better.” – Elon Musk

The customer feedback loop

There’s nothing worse than spending a lot of time, money and resources going in the wrong direction. That’s why it’s critical to meet with people you know who are products owners and users during the development process to either self-reinforce that you’re on the right track or self-correct early on when the risks are lower. That need highlights one of the ongoing struggles; engaging with people who are actual users. It’s one thing to validate early concepts with people who might “represent” your users, it is quite another to engage people who are well-profiled, carefully segment users of your products.

Connecting customer insight with product decisions helps you:

1. Prioritize product features and investments
2. Validate the go-to-market strategy
3. Create a better user experience to strengthen the customer journey, improve brand loyalty and increase customer lifetime value

Being able to validate users’ needs early on – and often – can ensure your development team isn’t making assumptions that are left unchecked and steer the solution off course. The longer you wait to get user feedback, the harder it becomes to unravel your process to get it right.

How product teams get bogged down

In product development, there are many ways you can collect customer feedback.

• You can conduct a focus group.
• You can conduct customer interviews.
• You can create a customer advisory board (CAB).

Agile insight is critical to product development. This is how product teams can solve real pain points. However, traditional methods of direct customer feedback are simply too slow, cumbersome, expensive and unreliable. Customer feedback often fails to keep pace with the speed of business. The feedback product teams receive also tends to be anecdotal and coming from a small group of customers who may not reflect the majority. Third-party data doesn’t go deep enough, siloed transactional data lacks context and user metrics only tell part of the story.

This forces product managers to use research only to validate decisions after they’ve been made, or worse, not use data at all. The outcome of slow and expensive data is decisions made without data – decisions made on gut instinct, opinion, or anecdotal feedback from a very small group of people.

The most impactful way to shorten the customer feedback loop

According to Mind the Product, 49% of product managers say conducting proper market research to validate their product ideas is their number-one challenge. Due to their inability to access timely customer research, they don’t know if they’re building the right products with the right features.

Imagine you had:

• Fast, agile access to highly engaged customers
• First party data
• Actionable insights
• Faster innovation cycles

The most impactful way to shorten the customer feedback loop is to interact with a large population of opted-in, highly engaged users. Well profiled, carefully segmented users who choose to participate because they recognize that their feedback can measurably improve the products they use.

On this surface, this appears to be a daunting task. The challenges of recruitment and ongoing engagement are obvious. However, the competitive advantage of a repeatable user engagement model is palpable; shorter feedback cycles, more confident, data-backed decisions, rapid iteration not to mention increased customer loyalty and trust.

The speed and availability of agile insight is a big issue. Finding customers who are actual users exacerbates that issue. Product teams need feedback often and quickly, which isn’t possible with the slower traditional research methods. But finding a large group of engaged customers, particularly hard-to-reach audiences, isn’t as difficult as is it seems. Yes, people today are easy to reach but difficult to engage. And yes, keeping them engaged overtime is also challenging. Users know they value of their feedback and they want to know if their feedback is being utilized. They want to be assured that when you solicit their feedback you acknowledge that you know who they are. Customers want to be consulted, they don’t want to be spied on. This requires transparency and clarity. Also embracing opportunities to share back information about the impact their feedback has on the product road map also builds trust and drives engagement.

“The wisdom of many is preferable to the brilliance of one.” ― Roman Pichler, Agile Product Management with Scrum: Creating Products That Customers Love

Many brands have found that investing in a repeatable engagement model is the right solution. A customer insights platform like Sparq enables you to build genuine relationships with customers, offer them more value and engage in two-way communication, which all lead to higher engagement than traditional research methods.

Some of our customers, like LinkedIn, VMWare, Jam City, Stanley Black & Decker, Audible, Goodyear and GoDaddy leverage Sparq specifically to shorten the feedback loop in product development. It gives these brands access to opted-in, well-profiled customers. Product teams have access to these customers because they have been given the permission to engage. In turn, this accelerates the development process. Users stay engaged because they see the results of their feedback; both in product enhancements and the results that are shared on members hubs, newsletters and more.

In The Total Economic Impact of Vision Critical, a study from Forrester Consulting on the ROI of the Vision Critical platform, one customer estimated their company saved between $2 million to $5 million from the development and marketing of a flawed product by engaging their customers in the innovation process.

Gain agile, scalable feedback and ensure a minimum viable product

Agile product development has proven to be effective. Introducing shorter feedback loops into these systems will make them more effective.

We’ve highlighted two examples of customers who use insight communities to shorten the feedback loop for agile product development.

Example 1: GoDaddy

GoDaddy always understood the value of engaging customers for product development inspiration. The problem was the company’s market research and product development existed in silos. Data for development was collected in waves by surveying the entire customer base.

GoDaddy needed to gain greater accessibility to customer data that could actually contribute to product development. The team created the GoDaddy Customer Council, an opted-in community that allows customers to decide what they want to comment on and how they want to interact with the company.

By getting their customers involved in product development from the start, data was able to direct executives, and money was better spent on products that were vetted by those who will determine its success.

Example 2: Keurig Canada

Keurig Canada uses the Vision Critical platform to engage with thousands of customers to get feedback for all product decisions, big and small. From the initial idea to packaging and distribution, customers are involved in every step of the product development process.

This close relationship with customers is what has helped Keurig Canada maintain its leadership and competitive edge in the space. Consumer insights analyst, Eileen Chen, said, “The real benefit of an insight community, especially for a CPG company, is with our innovation across the whole pipeline.”

The community allows the company to be iterative, and the product team often conducts as many as four rounds of feedback for a single project. The feedback the team receives is current, which is exactly what helps them course correct and make adjustments in real time.

By leveraging a community of opted-in and engaged users, just like GoDaddy and Keurig Canada, you can gain agile, scalable feedback and you can introduce this customer-validated feedback at every stage of the product innovation cycle. Your agile product team will be able to learn fast and make adjustments earlier and more often.

Regardless of your methods, reliable ongoing feedback is going to accelerate the product cycles, increase your hit rate and make it easier to prioritize workflow and allocate resources.

In 2018, Experience Management (XM) arrived. Customer centricity is now a long-term, strategic imperative at the executive level. With one successful IPO, one mega acquisition and accelerating consolidation, 2019 will see the Data Management Platforms (DMPs) take over as the sources of truth in the enterprise.

That takeover will drive significant change across corporate structure, technology and data workflows.

For customer experience professionals – those activating the insights customers offer – the winners will be those who earn the right to work inside the system of record’s firewall, engaging the right customers to share data and opinions that can build upon what’s already known by the enterprise.

It’s that simple.

As this becomes reality, four corporate behaviors will become prominent.

Companies will reorganize around the customer

It’s already happening, of course. We see cross-functional teams coming together to form an “enterprise perspective” on gathering customer insight, knowledge and feedback. But in 2019 and beyond, this behavior manifests itself in larger, multi-year commitments to derive massive value from putting people, processes and tech all against a common goal – improved business outcomes based on organization-wide customer-centricity.

Consider The Wall Street Journal’s report, Management Top 250, an annual report of companies that excel across a number of categories. All found success by focusing primarily on customer-centricity. Ranjay Gulati, a Harvard Business School professor, added that, “companies that are more customer-centric innovate in more powerful ways, leading to more engaged employees and strong financial performance.” That’s how executives are measured.

Insights will get married into systems of record

Insights will only scale – delivering their maximum value – if they can add value to the system of record. Otherwise, they’ll be left out of the engines that seek competitive advantages that can be leveraged through products, communications and experiences. In the year ahead, stand-alone insights will decrease in value as fast as the traditional market research firms have lost their enterprise value in the public markets. PowerPoint won’t die, but we will begin to accept that more insights are stale by the time a slide is produced.

Innovation will become repeatable

Deriving value from large DMPs means that processes must be repeatable for companies and the partners they work with. Everything from customer engagement models to data collection processes must be able to be done, easily, day-in and day-out. Yet being innovative and scalable has always been a problem for insights and customer engagement platforms. That paradox will be overcome by the most progressive, innovative people in our industry. It’s a great opportunity.

Companies will control the insight process

At a functional level, a company can’t embed repeatable insights into a system of record or rely on continuous, real-time outputs from it unless those data and systems are self-managed. Further, executives know that a business can’t successfully outsource building upon an existing internal source of truth. And finally, trends in privacy and security will require trusted insights professionals to work inside the firewall. Ultimately, these factors mean that more insights professionals will either join highly trusted advisory firms (who already are approved to work with an organization’s sensitive data) or move to the customer-side in order to continue their work.

Of course, that means that new skills will be demanded of insights professionals. Again, a massive opportunity. Systems of record will use artificial intelligence (AI) to evaluate the quality of data coming in. The considerations around quality will shift from the method to the source – a natural, but potentially challenging professional opportunity for many of us.

Enterprise leaders are realizing that the value they get out of customer insight is directly related to infusing those insights into the core of their business – their system of record. Without a doubt, this is a threat to the way insights have been gathered and leveraged for decades, but it is an amazing opportunity for the professionals who seek to put the voice of the customer prominently in the decision making that organizations make every day.

We wish you all great success in seizing this opportunity in 2019.

It used to be that traditional methods of market research, typically slow and costly, were the only ways customers could help companies better understand them. The market research department was often isolated from the rest of the business and the speed they operated at rarely gave them momentum to deliver actionable insight.

It’s now everyone’s job to understand the customers as businesses strive to be customer-centric organizations. Collecting and analyzing customer information are done in several business areas, but does everyone understand the nuances between customer intelligence, customer data and customer insight?

Customer Data

Data is the raw material of information about customers. It can be biographical information like age and education level, a single response to a survey question or a discrete record of a single purchase.

Data is essential but on its own largely useless – it’s backward-looking and can’t predict how customers will behave. Context gives data meaning.

For example, the fact that a customer bought your product twice this month is uninteresting by itself. The fact that a customer bought your product twice this month but only once last month is more interesting. The fact that a 30-year-old customer with a master’s degree bought your product once last month and twice this month is more interesting still, and so on.

The more data you can contextualize, the more your data can deliver intelligence.

Customer Intelligence

Intelligence is the holistic and flexible understanding of customers that comes from gathering, contextualizing and analyzing data. Intelligence is data studied and scrutinized to produce actionable insight. Customer intelligence means placing information into context, so we learn that our 30-year-old grad-school-educated customer has recently moved into the neighborhood and has a young family.

To gather customer intelligence, you need to draw on data from multiple sources and analyze it at the speed of business, in real-time. This intelligence tells decision makers “who”, “what”, “when” and “where”. 

Customer Insight

Insight is the deep understanding of customers that comes from gathering, analyzing and synthesizing customer intelligence. Insight goes beyond the “who”, “what”, “when” and “where” to tell us “why” customers behave as they do, guiding better business decisions and delivering results.

Arriving at insight means learning that our well-educated 30-year-old customer with a young family is buying your brand of diapers because the family includes a baby. We learn that the family is also occasionally buying a competitor’s diapers because they’re cheaper and the family is on a tight budget after moving into a new house. Perhaps we decide to build loyalty between this customer and our brand by offering coupons for our diapers to lower their cost and help the family through a financially difficult time.

Customer insight takes your intelligence, made up of data you’ve collected from various sources, and delivers actionable insight guiding towards opportunities you may have otherwise not been aware of. It’s the crucial element needed to be a customer-centric business.

Understanding these differences is only part of the challenge of surviving in the era of the empowered customer. Check out our guide and learn how you can help elevate your business to a new level of customer-centricity.

The retail industry is only set to become more competitive. Yet, most companies are still failing to meet customer expectations and become more customer-centric. According to Accenture, this is because there is a growing disconnect between what customers need and what retailers are providing.

Customers today want more than a transactional service; they expect a meaningful interaction with a brand. Retailers are still focusing too much attention on their products, pricing and margins. In order to survive, they need to ask themselves the fundamental question: “how can I serve my customers better?”

QVC is a leading video and e-commerce retailer, engaging with millions of shoppers every day. They combine the best of media, retail and social to create the most customer-centric and rewarding shopping experience. We caught up with their insights team on their priorities for 2019.

Evolving retail proposition in a changing market

In today’s retail market, customer expectations are constantly evolving and new market entrants continue to disrupt the landscape. QVC identified the need to get closer to their customers to understand what they want and expect from a multi-channel retailer. This insight empowers QVC to be more reactive and agile in their roadmap and attract new, younger audiences whilst still catering to their traditional customer base.

Their insights team regularly evaluate their offering against competitors to identify whether customers are getting anything elsewhere that they don’t have access to at QVC. This is currently a focus with their Christmas research initiative, ‘12 Days of Christmas’, with each day dedicated to understanding the strengths and weaknesses of a particular area of the business.

Allowing your customers to be a part of your business transformation

Building an authentic relationship with your customers allows them to influence the ways in which your business changes and grows, which is important for any customer-centric retailer. QVC closes the feedback loop by injecting customer feedback directly into key business decisions as they are being made. This, in turn, means that they can demonstrate the value of their customers’ feedback by investing in simple changes to their brand experience that their customers have actually requested.

“One recent example involved asking our loyal members which types of content they wanted to see more of going into the new year, and the response revealed that they were eager to see more interactive content surrounding ‘deal of the day’. This was something we were able to incorporate into our content strategy right away and enabled us to share back to members that their feedback had been actioned.”

– Keira Henderson, Customer Insights Researcher, QVC

“The experience” is your USP

Industry-leading content everywhere is advising customer-centric retailers to invest in making their customer experience unique and memorable. Now that all retailers are competing with key players such as Amazon, they need to differentiate themselves with their USP. QVC focus on a unique combination of retail and content to make their customer experience as curated and customer-centric as possible. Looking at larger online retailers, the buying process is often heavily transactional whereas QVC is interacting with their customers and creating a social experience. Within their community, the QVC Lounge members log on daily to discuss their recent purchases with the team and even to engage with fellow members.

Consistency and value across multiple touch-points

Shoppers are now experiencing your platform from multiple devices and touch-points, and each touch-point needs to demonstrate brand consistency and deliver value. Brand consistency means ensuring your customers have a positive experience every time they visit your platform by creating a seamless customer journey. For QVC, this is particularly challenging since they also cater to teleshoppers and one of their ongoing missions is to create a seamless multi-platform customer journey. The insights team discovered that this can only be successful if you are asking your customers what they want from different platforms on a regular basis. QVC segment their audience based on how they interact with the brand (e.g. TV viewers vs. online users) and tailor their offering based on the powerful insights from these groups of individuals.

Conclusion

Customer data is all around us. Most retailers have access to plenty of data but lack a defined strategy to fully understand and utilise the data. To gain a 360-degree view of the customer, companies need to understand the context behind the numbers – the why behind the what. Once you understand who your most loyal customers are and what these customers value in your brand, you will know what to modernise and how to evolve to remain successful. Whether this is a small change to your customer journey or a product road-map upheaval, listening to your customers and validating their feedback is the only way to look after the bottom line in the long-run.

Data science. Look-alike modeling. Training predictive models.

Sounds complicated.

But these are hot topics especially for progressive insight professionals who are looking to deliver even more value to their businesses. With great examples and advice from Canadian Tire, I hope to demystify those topics and show how insight professionals can support marketing and product decisions around customization and personalization that today’s consumers demand.

Before that, I’d like to extend a big thank you to Cedric Painvin, Associate Vice President Consumer Research, and Colin Huggard, Senior Research Consultant, at Canadian Tire, for sharing their data science journey and advice for success. For context, the Canadian Tire Corporation is Canada’s largest retailer with 1700 stores, owns multiple brands including Sport Chek, Marks and Canadian Tire, and generate over $12.6 billion in annual revenue.

Getting started: bridging silos

Canadian Tire’s insight community, the Canadian Tire Customer Panel (CTCP), is a key component to bridging data silos. Canadian Tire is in the lucky position of having millions of people in their loyalty program and an engaged insight community of close to 200,000 members. The loyalty program’s unique identifier forms the bridge between:

• Purchase behavior collected via loyalty member transactions
• Demographic, psychographic and attitudinal data collected via CTCP

With this connection between the loyalty program and CTCP, the insight team is able to develop unified customer profiles that are deep and granular enough to inform the level of personalization that customers demand, and can be leveraged by decision-makers across the business.

Assembling a can-do team

Being stewards of the voice of the customer and connecting the dots to transactional systems, Canadian Tire’s Triangle loyalty program requires a can-do team comprised of insight professionals and an embedded data scientist. The insight team follows a test and learn approach to deliver wins to the business and experiment with things that haven’t been possible before. No one on the team was initially an expert on how to use community data to enhance machine learning and data modeling – they had to learn their way there.

Combining information from the loyalty program to build on the growing knowledge gained about community members can really bring niche product buyer profiles to life which enables better targeting for marketing and special offers. With this paired knowledge, the team can often deliver value without intrusion – finding insight without having to reach out to their community members at all. For example, the insight team has profiled the demographics of brand buyers versus non-brand buyers for brands that Canadian Tire carries which include Motomaster, Mastercraft, NOMA, Canvas and Woods. This helped the brand teams have a better picture of who their customers are in order to help develop their long-term strategic plans.

Training Models and Finding Look-Alikes

Pushing data out of the community and into the transactional system makes two things possible:

1. Smarter machine learning and an anchor for the predictive model
2. Look-alike modeling, where results from a small sample of community members are extrapolated to like-type customers in the wider customer database

Retailers are trying to personalize their messaging and offers to the right people at the right time. Here are three questions Canadian Tire’s insight team was able to answer by building on what they already know from their transactional loyalty program data and CTCP:

Are we sending targeted messaging to the right person?

Trade Pros, including plumbers, electricians and contractors, are Canadian Tire’s high-value customers. It’s hard to determine whether someone is a trade pro based just on their transactional information. Through an iterative process, the insight team asked a hypothesized group of trade pros about their profession through the community. The results enabled them to remove the people who were inaccurately categorized, with profession becoming the anchor to train and improve the model. They were able to successfully identify a sizable trade pro segment in their loyalty data. These people spend 3.7 times more and visit the store 3.6 times more often, so knowing who these customers are and improving targeted messages will have a sizable direct business impact.

What’s the right time to send a “just moved” offer?

Data scientists had the anchor they needed to train their data based on inferred geolocation and translations data but needed to validate their hypothesis that a customer had recently moved. They simply asked people who they thought might have moved if they actually had moved. The result is an algorithm that will deliver more relevant offers at the right time (after recently moving) to the right people (recent movers). They’ve also started experimenting with predicting when someone will be moving, based on what they’re buying and when.

How could we customize messaging with the right message using look-alike modeling?

Colin shared two great examples of look-alike modeling:

• Customizing marketing communications about the benefits of Canadian Tire’s loyalty program and credit cards. They talked to customers in the community about messages and benefits that were relevant to them. Then they found like-type customers in their wider loyalty database and sent them improved marketing messages through digital channels. The blend between survey results and loyalty data resulted in more impactful and effective communications. They’ve seen double the conversion rate, 30% savings in cost per thousand impressions and half the cost per acquisition.
• Delivering customized value-based digital marketing for automotive messaging. Canadian Tire wanted to personalize automotive messaging so they worked with a research vendor to do consumer segmentation based on attitudes, behaviors and mindsets for the automotive category. They used the typing tool (key questions that drive categorization) to recreate the segments within their community. Since each member of the community is also part of Canadian Tire’s loyalty program, their data scientists can layer the transactional information on top of the segmentation to identify the behavior patterns that are most likely to occur within each segment. Once they connect the dots between the segment and the behavior, they can project the segments out to the entire loyalty program universe. Added bonus: the progressive profiling of community members over time will continue to build and improve the predictive model. So within the loyalty program, Canadian Tire can then identify who is the best fit and what is the most profitable segment for specific products and campaigns. Their targeted offers again become more relevant versus simply basing offers on sales data alone.

These projects have had direct and tangible results through more effective marketing, a clear impact on revenue and a growing appreciation throughout the organization of the community as a strategic resource.

Advice for starting out on the machine learning and look-alike modeling journey

Colin shared his advice for getting started:

Start simple. Results that might seem obvious or dull to researchers are likely to be mind-blowing to other stakeholders in the business. Either because the answers are helpful or because it fires their imagination for what else is possible.

Track everything. Institutionalize tribal knowledge about the process, learning and metrics. This helps to demonstrate learning and quantify what improvements were made during the process, especially when things don’t go quite right or as expected.

A/B test everything: Take any opportunity to A/B test or see what pulling on different levers can do. Leave no stone unturned. It’s all about learning and uncovering new insight.

Socialize: Share the results widely. Drawing attention to our successes isn’t just an ego thing, it will wake people up to the potential of customer panels and big data, and you will likely end up with very actionable requests coming from unexpected places.

Wrapping up

Translating the insights into a revised marketing communications strategy is the vision of where Cedric and Colin want to go to support Canadian Tire – developing better-targeted offers, briefs, messaging, content, imagery, copy and more. The CTCP paired with innovative machine learning and look-alike predictive modeling tactics are helping to bring this closer to reality. It’s great to see two typically siloed teams starting to work more together as partners for delivering customer insight to their business.