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Jan 23, 2020
(No time to read the full article? Check out the Key Takeaways at the bottom.)
In Part 1 of our Customer Journeys blog series, we talked about how and why it’s important to collect and de-anonymize those valuable digital interactions. A good journey discovery process allows organizations to go beyond simple fan generalizations such as ‘my fans tend to be males between 25 and 45 who have families, are middle-management in white-collar jobs, like basketball and hockey, prefer Ford vehicles, and have a household income of at least $80,000 per year’. When you build journeys, those generalizations become specifics related to how fans purchase – the ‘male between 25 and 45’ becomes Leon Goldburg who generally buys tickets in Section K, 2 to 4 times per year, visits the team website every week to watch recent highlights and check player stats, who opens your newsletters 54% of the time and always clicks on value-based ticketing promotions. Identifying the habits of a fan (or group of fans) leads to understanding which touchpoints are most impactful in their journey and path to conversion. So, what’s the importance of stitching these touchpoints journeys together?
Arguably, Marketing will be the biggest benefactor in better understanding fans’ behaviors and connecting those online and offline behaviors and data (interactions and actual purchases). Marketing teams who are making it a priority to understand the patterns and trends in how their fans interact with their property as a whole find themselves using advertising dollars more efficiently, driving higher conversion rates, shortening the length of a customer’s path to purchase, and increasing engagement throughout their fan journey.
Likewise, Sales teams benefit from Marketing’s ability to deliver higher quality marketing qualified leads (MQL). By better understanding not only more about each fan but better identifying where they are in their purchase journey, Marketing teams can better qualify which leads are ready for Sales to begin directed outreach. Ticket sales are a great example, but these concepts transcend any purchase – be it merchandise, non-game events (i.e. draft parties or charity poker tournaments), sweepstakes entries, even simple newsletter sign-ups, etc.
For example, Leon Goldburg’s behaviors online may show that compared to every other fan who has purchased a flex pack with the team since tracking journeys, he is at the right stage for a sales rep to contact him about an offer. For the Sales team, understanding where Leon is in his journey based on his behaviors and identified channel affinity can help them better personalize or customize a package that would be most enticing and when to offer such a package. If we know that Leon purchases tickets in Section K, 2 to 4 times per season, clicks on value-based ticket promotions in the team’s newsletters, and likes coming to the website every week to watch the highlights. Perhaps, when a sales rep reaches out to him about a package offer, they ask him how his seats have been in Section K and offer him similarly priced (or even more economical) tickets in Section H, which might have better vision of a big screen (to see replays). From an aspirational perspective, that’s the level of understanding and service that all teams should strive for and also how marketing and sales teams can work really well together.
YourAnalytics teams help here by ensuring not only that pixels are dropped onto pages correctly but also to help uncover trends and points of friction in the customer journey. When trends suggest that a high number of people drop off at a specific stage in the journey, it’s up to Analytics to be the detectives and uncover what’s going on (which fan segments are dropping off and which are increasing engagement).
Stitching customer journeys together begins by pulling together all the data from all systems with which a fan interacts. Key points to note within the data are the timeline between and frequency of distinct touchpoints (website visits, email opens, ad clicks, ticket or merchandise purchase, etc) for a fan. With this data stitched together to show the timeline of a fan’s interactions, organizations can break this journey down into distinctive portions which help identify paths to purchase as a fan moves along their journey. While paths to purchase for a specific fan can vary depending on the time one requires to make a purchase decision (season tickets may be a longer decision process than a team t-shirt), fans will exhibit repeated patterns in their decision making and paths to purchase. The customer journey, and by association, the customer path to purchase becomes more defined and granular, the more data an organization can unify into a single customer view encompassing all online and offline touchpoints, across devices, platforms and channels.
Given the unique challenges of the sports and entertainment industry, such as the growing availability of entertainment options, relentless advertising across all platforms, and a need to cut through all the noise to find and maintain an organization’s fanbase. Stitching together fan journeys and the paths to purchase contained within them, departments across an organization can discover the answers to their most burning questions. How and when did a fan find your organization and interact? Which ads do they interact with for a specific promotion before purchase? When are they ready to buy? Where am I losing them? Why am I losing fans in their journey? How often do they look at merchandise prior to purchase? Answers to these questions inform an effective multi-channel marketing strategy, while providing transparency when it comes to attributing sales or revenue to campaigns.
One thing we see a fair amount is a discrepancy between ‘conversion’ metrics and true revenue from a purchase (be it tickets, merchandise, etc.). Marketing metrics can sometimes tell one story while actual revenue can tell a much different one. In order to save time cross referencing all the conversion metrics to actual sales, organizations need to tie their ticketing data to their customer journeys. There are multiple ways to accomplish this, including using Google Analytics or Omniture to track your pixels. The limitations with these options is that they still require that an organization manually build reports in another tool that cross references the specific pixels on the checkout page to ticket revenue, along with matching the known audience segments. Customer Data Platforms, however, are not only able to automate the process to tie those journey touchpoints back to ticketing revenue and identifying who purchased, but add an additional layer of insight by also analyzing trends within the data to predict future interests, and product fit . We talked about this in part 1 of the series in more depth.
Another question we get asked besides how to better attribute conversion metrics to true revenue, is how far back can pixel tracking go. For organizations, tracking goes as far back as when you first dropped in your pixels. Technically, if you had your pixels set up from day 1, you could track as far back as the user’s last time they cleared their cookie history. One major consideration is how well your pixels have been set up. If that wasn’t done correctly, all the great insights you could be tracking will be negated. Assuming your pixels are already up and running and they’ve been set up correctly, the next question is: How far back should the tracking go? The year-over-year data hasn’t been researched in-depth in the industry yet, however organizations that have captured 90 consecutive days have been able to start seeing trends emerge and highlight areas that need to be further investigated.
As organizations begin to de-anonymize fan behaviors by tracking anonymous digital interactions and connecting them to the interactions where a fan, like John Smith in our previous blog, has provided identifiable information via contest submissions, newsletter signups, etc, the next step is to stitch the journeys together in order to understand the paths and patterns that start to emerge. What are all the things that fans are seeing about your property and interacting with before they take action and convert, which segments are most likely to take a certain path? Understanding the path and timeline fans go through before making a purchase is important to better nurturing different segments. Analytics, marketing, and sales teams all benefit from these insights and each play a role in growing customer lifetime value and fan affinity.
Marketing is likely the biggest benefactor of customer journeys. Not only do they start to learn fan interests, preferences, and key touchpoints along the path conversion which allows them to provide more personalized advertising, when and where it resonates most with a certain fan segment, but they can also deliver MQLs to sales at a more optimal time.
Sales benefits by getting leads that are more likely to be ready for direct contact. Not only are leads hotter, but leads come with added insights into the fans. What are their tendencies, when do they generally purchase, what else do they keep coming back to on the website or through digital advertising? All this information can help sales reps build more trusting and informed relationships.
Analytics teams are the detectives. They use customer journeys to expose valuable trends, stages with high rates of drop-offs to investigate to further segment how groups of fans naturally convert.
Conversion versus Revenues. Conversion metrics are helpful but it’s imperative to understand true revenue (and product purchase breakdowns) for each campaign.
Optimal Tracking History. Tracking starts when you first drop pixels onto the web pages. Although year-over-year research is few and far between in the industry, 90-days of consecutive tracking can start to show valuable trends and patterns emerge.
In part 3 of our customer journey series, we tie it all together with how to action on customer journeys.
In the meantime, reach out to us with questions or to learn more about how we’re powering these journeys for sports & entertainment organizations.