The journey customers take when making a purchase is rarely linear and often fraught with obstacles. Here’s how data can help you better understand your customers’ path.
Data can tell you a lot about your business. It serves as the anchor to nearly every business function, and it dictates steps of the customer journey in marketing by humanizing consumers in the eyes of a brand. Without the use of data collection in business, you’d never learn enough about your contacts to help build audiences for targeted marketing campaigns.
Although it’s a smart idea to use customer data and analytics, businesses of all sizes continue to struggle to draw actionable insights from this wealth of information. And with so much data now available and being delivered from an ever-increasing number of sources, it’s nearly impossible to aggregate this information while ensuring its quality — especially if you’re going at it alone.
Using platforms like Pearl’s Customer Data Platform, or CDP, you can aggregate, cleanse, enrich, and organize data from disparate sources to provide analytics. You can then use machine learning and artificial intelligence to provide insights and modeled outcomes. This process facilitates using proven methods for making informed decisions.
Managing and Growing Your Business With Data
How data is used in business depends on the goals of that business. Consumer data can tell you basic information about a target audience, and social affinity data can shed light on interests, social behaviors, and other psychographic details — think attitudes or aspirations. Add transactional data, which includes product orders, order values, and purchase frequency, and you’re now getting a clearer picture of your target.
You can “humanize” consumers even further by pulling in consumer lifestyle data like what they watch and read, where they travel, and how they generally spend their days. There’s also geospatial data, which can tell you more about consumer purchasing behaviors in geographic areas as well as the saturation rates of marketing within a given location.
The amount of consumer information available runs deep, and these data points can combine to create a rich profile of each customer. The more attributes you can assign to a customer, the more targeted and relevant your marketing will be — leading to greater marketing performance, conversion rates, and customer lifetime value.
But the future of using customer data isn’t limited to building various target audiences. It can also be used to solve problems within an existing database. By tracking and reviewing data, you’re able to determine the success levels of a campaign, for example. This can be a crucial element of onboarding new customers and retaining current ones.
When making decisions about how to market and grow your brand, numbers and hard facts can solidify and strengthen sales and marketing campaigns. Hard numbers give you a true picture of your performance and how you might need to adjust your marketing efforts for better results. By better understanding the market, you can be proactive about what you offer in terms of products and services to your customer base.
Overcoming Data Challenges
While data insights can be wonderful for building and managing a business, data use isn’t without its challenges. For one, it can be difficult to identify the “right” kind of data to use. Some data is simply inaccurate or out-of-date — or rife with formatting issues, making it difficult to interpret or activate.
Integrating large datasets can also be problematic. If you’re trying to access and integrate data from multiple sources, you may end up spinning your wheels while also running into obstacles validating data or testing various data insights based on these learnings.
There’s also the issue of data privacy. Any information you collect must align with global, regional, and state regulations. Overstep this boundary, and you’re only looking at potential fines as well as a loss of trust from your customer base.
As the saying goes, “Garbage in, garbage out.”
When a database is composed of “garbage,” the marketable actions are either going to be inaccurate representations of the customer database or fall flat because the data isn’t viable. You’d be better off going with your gut than relying on bad or “garbage” data. The odds that you’ll get it right are about equal.
If you really want to overcome customer data challenges, it’s best to enter the space knowing the qualities of a healthy customer database. Healthy customer databases tend to share four key characteristics:
1. Verification. Names, addresses, positions, and other details have all been verified as correct.
2. Deliverability. All addresses will reach their intended recipients.
3. Compliance. All data complies with the General Data Protection Regulation and the California Consumer Privacy Act.
4. Enriched. Is there enough of the necessary information to properly market to an audience?
When data meets (or exceeds) each of these characteristics, data challenges become more of an afterthought — and you can more confidently proceed with your marketing efforts.
Understanding the Path to Purchase
The journey customers take on their way to making a purchase is rarely linear and often fraught with obstacles, diversions, and a dead end or two. Just one speed bump along the journey can be reason enough for someone to choose another brand. By mapping out that journey and understanding what we call the “Relationship Lifecycle,” or RLC, you’re able to seamlessly transition between interactions. Less friction improves the customer experience while increasing the likelihood of conversion.
Within the RLC, consumers generally work their way through six distinct stages:
· Stage One: Unaware
· Stage Two: Interest established
· Stage Three: Problem acknowledged
· Stage Four: Solution needed
· Stage Five: Evaluation
· Stage Six: Customer
Though it can sometimes take a lot of effort to move people from one stage to the next, they generate an unprecedented amount of customer journey data along the way. This data can provide valuable insights into patterns, habits, and behaviors — potentially accelerating the path to purchase.
The first stage is the most straightforward. These are anonymous “passersby” who’ve stumbled upon your brand. While it’s impossible to know exactly who these people are, it’s possible to capture data that includes certain demographics (age, gender, location, mobile vs. desktop, etc.) that can help you understand the starting point of their customer journey.
Once interest is established, people become “acquaintances” — a relationship that is new and somewhat nebulous. That’s why data capture is critical at this stage. This relationship may become one of “fan,” “friend,” or “family” — especially if they’ve opted into sharing information (“fan”) or have decided to make a purchase (“friend” or “family”). You’re able to capture greater details on buying patterns and the likelihood of them making their first purchase or purchasing again.
Because no two people take the same customer journey, this is the stage where you start to see crossover. You likely have some fans mixed with friends mixed with people already in your brand family (i.e., made multiple purchases). Information is readily available at this point, offering a deeper glimpse into each person’s interests, hobbies, and more.
The fourth stage is just as it sounds: Consumers have identified a problem and are now circling all the alternatives. You’ll find fans, friends, and family at this stage. Considering how much people like to keep their options open, you’ll also find a few loyal customers. Plenty of customer journey data is available at this point, and those who’ve made a purchase will clearly offer up a treasure trove of information.
If consumers have yet to make a purchase, it’s at this stage that they’re seriously considering your brand. Knowing what to say, when to say it, and which channels to use can tip the scale in your favor. None of that would be possible without data.
Once consumers become customers, you can capture and maintain their data. It’s also at this stage that there’s the potential to increase shopping cart value, encourage some word-of-mouth marketing, and reduce the chances of churn.
At Pearl, customer journey data — particularly data captured during the third, fourth, and fifth stages — helps our platform convert statistics into usable, marketable opportunities. We see data as an anchor for marketing, humanizing consumers, and allowing for more informed business decisions. With our platform continuously refreshing and reanalyzing data, each subsequent campaign becomes even more targeted and relevant to its intended audience.
Making Data-Driven Decisions
For many businesses, there’s a gap between data and data-driven decisions. Companies might be uncertain about pulling together a 360-degree view of the customer and then using it to drive their business initiatives.
Part of it is due to the data itself. If companies don’t regularly update, verify, and enrich their data, they can find themselves looking at a fragmented picture. Businesses need a richer, more detailed view of customers to make informed decisions, and that only happens by enriching first-party data from a third-party source — and ensuring its accuracy.
Even when data quality is deemed “good” for commencing analytics, what really widens the gap between data and data-driven decisions is execution. Many businesses come up short when it’s time to integrating data into their business strategies, but the solution is relatively simple:
1. Start with the end in mind.
You’ll be hamstrung to get anywhere without a destination. Step away from the data — and the overwhelming possibilities it can provide — and take a moment to identify your goals. Look at your business objectives, build a strategy around them, and then turn to your data. It’ll be much easier to weed out the data not relevant to your business or job function.
2. Identify key business areas.
Data is flowing into your organization from a variety of directions (customer interactions, sales, third-party providers, etc.). Start by managing the multiple data sources, and then identify which business areas will benefit most from this data. Are certain business functions key to your overarching business strategy?
3. Target datasets.
Targeting datasets will help you answer any lingering questions about how to address business issues. This process involves a review of your data sources to identify which ones provide the most valuable information. Just remember that data silos (each department collecting its own data) can lead to inaccurate reporting.
4. Collect and analyze.
This might seem obvious, but you need to identify the departments that will collect and manage data. You’ll also need to determine how to pull together data from different departments — as well as any data from outside sources. Taking this extra step ensures a well-rounded view of what’s going on across the business.
How you choose to analyze your data will vary, of course. Simple analytics may require no more than a working knowledge of Microsoft Excel. More complex analytics often requires a specialized skill set and an analytics platform to draw actionable insights from the information.
5. Turn insights into action.
The ability to turn insights into action often comes down to presentation. A number of business intelligence tools can pull together complex datasets and present information in ways that make insights more digestible. It’s much easier to see what actions you need to take to achieve your business goals.
These five steps should help you close the gap between data and data-driven decisions. The next logical step is right there in front of you — all you need to do is act.
Using Data to Inform Business Decisions
In their earliest interactions with consumers, businesses either neglect potential customers or inundate them with meaningless messaging. You need to humanize not only the data but also the interplay between customers and your brand. You’re trying to solidify a lasting relationship with customers rather than overwhelm them with unwanted or irrelevant messaging.
Think for a moment about the typical online shopping experience. You visit a website, and the first exchange is usually an offer for a discount in exchange for joining a mailing list. Let’s say you consider taking advantage of the 20% percent off coupon, sign up for the mailing list but ultimately decide not to buy — knowing full well you can buy later while taking advantage of the promotion.
Chances are, a day will not go by without an email reminding you of the merchandise you considered purchasing or a notification that your coupon will expire soon. You eventually get so jaded by those reminder emails that you delete them as soon as they hit your inbox — or block the business altogether.
If someone declines to make a purchase, there’s a fine line between prompting and pestering. Sure, he or she made an active decision to sign up for your newsletter. But when interest has been established and a problem acknowledged, it’s during these two stages of the lifecycle when you want to nurture the relationship and move it toward purchase or loyalty.
It’s here that the value of data shows its true colors. You’ve captured the information, so now it’s time to put it to good use by targeting your marketing efforts to build relationships. Remember that you’re walking a fine line, though. Don’t kill the opportunity before anyone gets a chance to get to know your business or your brand.
Using data to become more customer-centric in your marketing is a given. While the message will vary from one business to the next, the following tips can help refine any approach:
1. Personalize everything.
Personalization can be a struggle for businesses. A McKinsey study found that only 15% of CMOs say their companies get personalization right. But with 80% of consumers more likely to make a purchase from brands that offer personalized experiences, that 15% figure isn’t nearly enough. Set yourself apart from other businesses by delivering content — as well as products and services — that are personalized to your target audience based on engagements, likes, and past purchases.
2. Welcome new customers.
The value of data becomes clear when you welcome new customers. It can provide insights into how to position these campaigns, helping you understand how to customize each message based on past interactions — and showing you where and when to emphasize different aspects of your business. Data can also help you understand the appropriate cadence for communication, skirting the issue of potential spam.
3. Remember retention.
Conversion is one thing, but retention is another. Not only do you need to meet customer expectations with their first order, but you also must find ways to drive customers back to your business with even more personalized messaging. Using the available data, segment your customers and create retargeting campaigns designed to address their wants and needs. Expand your personalized messaging across three or more channels — businesses that do this report 90% higher customer retention rates than companies that stick to one channel.
4. Reach out to lagging customers.
As far as low-hanging fruit goes, look to past customers. They have a history with your brand, which means your messaging was effective at some point. Your goal is to get them to purchase again. Only one question remains: “Who will fall within your re-engagement efforts?” While you’re at it, set a time frame for your efforts to target these customers. Will it be one month? Six months? A year? This decision will inform your interactions going forward and ensure that your personalization efforts ring true.
The value of data in business can’t be overestimated. It can be exactly the boost needed for every aspect of your business operations. It all comes down to understanding how to use the available data to encourage the desired results — and it’s why we developed Pearl.
To learn more about how Pearl can help humanize data and enable you to make data-driven decisions around customer interactions, contact us today. A member of our team would be more than happy to explore your options and discuss potential next steps for using data to achieve superior business results.