Why CDPs, ESPs, and Marketing Automation Fail on Their Own

Just like any effective team, your martech stack can’t be a one-man show. Can you imagine resting the entire operation of a marketing department on one employee’s shoulders? The ensuing chaos wouldn’t be pretty. Likewise, it’s unrealistic for marketing technology buyers to assume there’s one platform that can solve all your challenges. CDPs, ESPs, and marketing automation tools have proven they can’t run a standalone show and grow your business successfully. 

Simple ESPs can’t handle the heat

Our Chief Growth Officer, Josh Francia, learned the hard way that ESPs can’t cope with high volumes and expanding use cases. During his time at an online travel giant, Josh’s team ended up breaking three ESPs. Ultimately, this was because the marketing team was piping in more data than ESPs on the market were built for. Unfortunately, these traditional systems couldn’t keep pace and broke. Imagine the revenue impact this had. Consumers are generating more data than ever before. Therefore, playing a game of musical chairs with ESP providers isn’t a viable solution. 

Automated, but not intelligent  

Piggy-backing off of single channel solutions like ESPs, marketing automation promises to streamline a marketer’s day to day by mapping out the customer journey and executing upon that plan, hands-off. But if your marketing automation platform is being fed siloed data, how is it supposed to execute a true cross channel journey? Additionally, basic automation systems also require that data scientists pull lists for marketers, which becomes a time drag, and often offers little to no intelligence feedback. So, marketers have an easier job of scheduling messaging but have no control over customer data, list building, and intelligent predictions

My data is finally in one place… now what?

The last solution that has been promoted as a bandaid for data-powered marketing is the Customer Data Platform. This platform promises to unify siloed data systems into one unified customer view, eliminating the need for additional engineering support and resources. But, no immediate actions can be taken without the aid of other platforms. Traditional CDPs, fundamentally, cannot stand alone. CDPs need additional marketing automation to function. 

CDPs, ESPs, and marketing automation may fail on their own. But a well constructed stack that uses a combination of these three platforms will transform your business. A solid foundation is laid by the CDP, AI-powered marketing automation gives it action, and finally, a channel app delivers that action. 

Our Customer Data Activation Platform is bridging the gap to seamlessly execute all three of these components. Want to see it in action? Contact our team.

5 Things We All Hate About the CDP Trend

There’s no doubt about it: the Customer Data Platform is the consummate poster child for martech in 2019. It seems like there’s  a new offering up almost weekly, and everyone is getting on board. Massive marketing clouds and the smallest of startups alike have added their version of the CDP to the mix, but as many of us have learned by now, they’re not all created equal — and this leads to a whole mess of issues. Here are 5 things we all hate about the CDP trend.

1. There’s little alignment on what they actually do

There’s a ton of literature touting the value of CDPs, but an overarching definition on the solution was only recently offered by experts. Originally, CDPs were designed to be both a database and provided an application to use in tandem (ie: predictive modeling). It was clear early on that the database was the most valuable component, and thus the purpose changed to a database that could plug into any application. Therefore, two categories of CDPs were born, say CDP experts: some just build our your customer database, and others both build a database and run this data through marketing and analytics programs. Until industry experts truly flesh out the categories of CDPs, and how to define them, it’s left up to marketers to seek out their perfect platform fit.

2. It kiiiinda seems like something you already have?

A lack of alignment on what a CDP actually is means that it’s easy to assume you don’t need one, or that the solutions you currently have basically add up to a CDP. Take DMPs, for example. While a DMP’s end goal might appear similar, these two platforms are far from identical. DMPs traditionally play in the adtech space, and can pull data from different sources (think 3rd party data) and categorize this data in order to target certain segments of customers with ads. CDPs collect and unify a company’s 1st party data in order to build 1:1 user profiles, which can be used for targeting, but also website personalization, email and push customization, and product recommendations.

3. The black box ethos is rampant 

Though AI is the buzzword of the moment, the average businessperson has little clue how it works. This is through no fault of their own, of course. Most companies offering AI, particularly in the CDP space, have a black-box approach. Nobody wants to give insight into the secret sauce. But marketers deserve a platform that explains outputs and helps with understanding the various factors and weights that contributed to it. It’s only through this level of transparency that marketers can better understand their business and what’s going to help it grow.

4. Those who over promise and under deliver  

Big marketing clouds that are eager to check all of their customers’ boxes sacrifice quality when they add on or acquire a CDP offering. Unfortunately, a patchwork of acquisitions and hastily built add-ons always turns out to be problematic. A true CDP will be purpose-built from the start, natively providing data unification for 1:1 marketing at scale

5. Building a solution seems like it would work — but it doesn’t 

It’s common to feel wary of the lack of customization or breadth of a new solution, particularly for Enterprise clients. This often leads to engineering teams attempting to build an in-house solution. Our Chief Growth Officer and former customer, Josh Francia, was one such buyer. You can read about his build vs. buy journey here.

Love or hate CDPs, they’re here to stay. If you’d like to learn more about our CDAP offering and how we can help you, connect with our team.

 

5 Ways Predictive Recommendations Can Transform Your Marketing

Increasingly, consumers are expecting completely personalized content when they’re online. While delivering 1:1 content to every user across every digital channel can seem next to impossible, bringing on predictive recommendations can transform everything from your user experience to marketing procedure.

1. Predictive recommendations keep customers online longer

We’ve all gotten sucked down the rabbit hole of excellent recommendations – a simple search turns into hours of browsing. Real 1:1 recommendations will leave your customers happily scrolling and discovering new products. This might lead customers to new products they wouldn’t have otherwise found on your site, but love nonetheless. 

2. Tailored recommendations make browsing a huge catalog easy

Sites with giant catalogs like Amazon may seem daunting to browse initially, their recommendations make it easy to find something you love. Blueshift customer, Urban Ladder, saw a 4x conversion rate from bringing on our recommendation capabilities.

3. Predictive recommendations create loyal customers

A big struggle of digital marketing is taking a traditionally offline market and adjusting it for an online experience, but it’s not as daunting as it seems. In the past, brick and mortar businesses that offered exceptional customer service and gave personal recommendations to each client experienced a high level of customer loyalty. Today, nothing has changed. Suiteness, a strictly online luxury hospitality company, has been able to step into the role of a travel agent and concierge for its customers – with stellar results

4. 1:1 recommendations can reheat customers who’ve fallen off

Some lost customers can seem impossible to win back. You throw the best promotions, and most clever and well thought out emails their way to no avail. 1:1 predictive recommendations can help provide real value to those tricky customers, and help lead them back to your site. A seamless recommendation engine that can cross over from email to website will play a major key in getting high-value, unengaged customers back on track and converting.

5. Predictive recommendations are responsible for revenue

Vouchercloud, the UK’s leading money-saving app, saw an 81% increase in revenue by using personalized recommendations across email and mobile push. 1:1 recommendations are no longer something that’s nice to have, they’re a necessity for driving growth. Learn more about Vouchercloud’s strategy here

Ready to learn more about Blueshift’s recommendation engine, and what it could do for your business? Contact our team, or learn more about our AI-powered recommendations here.

3 Tips to Avoid #Personalization #Fail Part 3

1:1 personalization isn’t just a lofty goal brands are considering attempting in the next few years. It’s here now, and consumers are starting to expect it as standard for their user experience. The biggest roadblock for marketers trying to deliver on those expectations? Sub-par tech inhibiting their customer-centric campaigns, which leads to comms that miss the mark. Check out some of the best #personalization #fail tweets of the last month:

1. Bring your data storage into the 21st century with NoSQL

NoSQL databases can better understand multiple data sources as compared outdated, sql databases. Conversely, traditional data storage makes it difficult for disparate data sources, such as your customer profile database and catalog database, to be understood holistically which leads to embarrassing fumbles like the one above. NoSQL databases can rationalize your siloed data in real time, which enables marketers to autonomously deliver relevant and accurate messaging to high-value customers.

2. Use AI that can target your biggest fans, not just Jane Doe

Don’t waste money going after shoppers who don’t want or need your products. Instead, utilize AI to forecast your most high-value customers and target these folks with personalized messaging that will keep them coming back for more. 

3. Soulmates, not twins

Use up-to-date insights and browsing patterns to influence personalized product recommendations, instead of vague selections based off of demographic insights. You never know who’s browsing! Seek out a comprehensive marketing platform that has the power to analyze website data in real time. So, families using one device, or just complex users, can get suggestions that impress and convert.

Ready to see how Blueshift helps customers avoid these common pitfalls? Connect with us today, or check out a few more tips on avoiding personalization failures here and here

 

Fast-Growing Real Estate Startup, Zumper, Scales Leads by 384% with Advanced, Predictive Recommendations

Finding a place to call home in a competitive rental landscape can feel like trying to find a needle in a haystack – except that haystack is also full of fake needles, spam, and a slew of competitors. So how can renters find their perfect fit, in their dream neighborhood, for the right price? 

Zumper’s mission is to answer such questions.  Their end-to-end rental platform aims to make renting a place to live as easy as booking a hotel. But, no two users have the same journey and as their user base grew, their marketing team struggled to deliver timely consumer centric marketing based on shifting user preferences and an ever growing catalog of listings. 

After legacy systems failed to meet their needs, and their engineering team exhausted their resources to build and maintain a platform in-house, they found Blueshift – it was a fast, flexible system that could keep up with millions of listings at any given time, and the team loved that it was AI-powered, rather than rule-based, because our recommendation engine that covered all their use cases. In a short time it became clear that Blueshift was the only platform that could check all their boxes and grow with Zumper simultaneously. 

Hyper-personalize and scale campaigns with advanced recommendations and triggers

Once the Zumper marketing team had made Blueshift their home, they set out to use our AI powered platform to revolutionize their user experience and put the power of customer data in the hands of their marketers. Blueshift’s ability to process Zumper’s vast amount of customer data in real time has surfaced some fantastic results for the fast growing real estate brand:

“Blueshift’s AI-powered recommendation engine allowed us to better serve our customers with targeted marketing and personalized campaigns at scale,” said Kristy Ng, Director of Lifecycle Marketing, “The platform enabled us to turn user behavior into experiences that capitalize on actionable insights that were critical to customer experience.”

The platform quickly provided value and became an integral part of the multi-channel marketing approach within two months of launch. Their lead submission grew by 384%, click-through rates skyrocketed, and message volumes expanded by 198% with no change to resources used by Zumper’s team – besides Blueshift, of course.

Next steps

After experiencing staggering growth with Blueshift onboard and scaling up 1:1 automated campaigns, the Zumper team plans to extend our personalization features onto new channels, including onsite content and paid campaigns on Facebook and Google, to continue to provide an engaging user experience – no matter where users navigate.

Take an in-depth look at how Zumper achieved success by downloading the full case study, or, if you’re ready to start investigating how AI might help grow your own marketing efforts, you can request a demo.