Our industry is ever-changing. Get insights and perspective from our experts as we share our knowledge and experience on how to successfully navigate the marketing landscape.
The number of marketing technology tools is rising at a staggering rate, having nearly doubled YOY in 2016. On average, 51% of organizations use 21 or more digital marketing solutions. While having the ability to access all this new information provides unprecedented insight, it frequently results in an overload of data siloed in disparate systems. Marketers are buried under a landslide of fragmented campaign metrics, products, customers, purchases, and more. And there’s seemingly no end in sight; Gartner reports that 50-65% of marketing executives plan to spend more on marketing technology in the coming year. To make meaningful use of all this data, metrics need to be reviewed cohesively. Without a full holistic view, it’s impossible to get a complete understanding of the real story. Only analyze existing customer behavior and you may miss out on opportunities to attract new audiences. Only review site traffic from media placements, and you lack an understanding of why your customers are loyal to your brand, or how to create more of them. The problem is that most marketing teams don’t operate with systems that talk to each other. They end up trying to manually analyze disparate data points to uncover insights, a practice that is neither scalable nor responsive in real-time. This data fragmentation is costing marketers real dollars as they lose the ability to effectively optimize campaigns and fold learnings into future plans. An example of how some marketing departments utilize data today: The solution begins with creating a connected data ecosystem. The concept is simple—collect all data points into a centralized system able to analyze them en masse and surface actionable insights in real-time. Using those insights, marketers can then start to roll out personalized content, translate strategies across all channels, and efficiently improve customers’ experiences. While the initial creation of the connected data ecosystem can be time-consuming, it pays off. One example: Annuitas Group reports that businesses that use marketing automation to nurture prospects report a 451% increase in qualified leads. Based on our experience with building ecosystems for clients, we have created a four-step process that we follow: Discovery: we assess all the various systems that are collecting data around your organization. We also identify areas where the data returned may be less than perfect in quality–a very common occurrence. During this period we also outline the shared objectives and definitions of success across all the stakeholders. Solution design: using our learnings from the Discovery period, we design a customized solution that aligns to your business objectives. We build a roadmap using multiple analytic approaches across our four service categories: Performance analytics, marketing sciences, research, and business intelligence. Analyze: This is where the magic happens. Now that we’ve designed your ideal system, we begin to collect the data and review the outputs using our team of data analysts, statisticians, and social scientists to uncover those insights that will truly give your business the competitive edge. Insights: This is when our team works with marketers and other members of your organization to communicate our findings and share recommended actions to meet your KPIs. Once the initial foundation is built, this process can be repeated multiple times over the course of the year, ensuring your teams are always up to date on the latest findings and fully able to use fresh data to inform future plans. The creation of a connected data ecosystem takes some effort but pays off almost immediately by making teams run more efficiently with a full understanding of the current state of their business and what levers they have to achieve their goals.
I recently read a stat from CMO.com that said 56% of consumers feel more loyal to brands who “get me” and show a deep understanding of their priorities and preferences. It reminded me of a conversation I had at an event with a woman who walked up to me and said, “Can I ask you a private question? Just what in the heck is a DMP?” Those two things highlight the disparity between the need for data-driven, personalized marketing and just how tough it is to figure out how to do it. The woman at the event who asked me that question is not alone – I get asked some form of it all the time and from really experienced, savvy marketers. They know that they need to care about data’s impact in their campaigns and they usually have some great ideas for how to personalize content so it’s meaningful for their customers. However, they are absolutely lost when it comes to understanding how to capture, analyze, and actually use the data. A data management platform (or, DMP) is extremely helpful for that, but it can feel overwhelming to figure out how to build and maintain one. With the number of MarTech tools growing exponentially every year, most marketers don’t even know where to start. Add to that the significant investment in money and time as well as a potentially foggy ROI and it can be easy to slip into analysis paralysis. Since I get asked so frequently, I thought it would be helpful to start with the basics on what the heck a DMP is and what kind of campaign results can be garnered using one. To start, a DMP is basically just a big database that stores a bunch of different data points on your current and potential customers. It can be populated using 1st, 2nd, and 3rd party data which is then combined to identify patterns in the way your target audiences shop, spend their free time, consume media, and move about the world. Using a DMP, marketers have the ability to get deep insights into their customers, such as what other retailers they visit and how frequently they are viewing their website versus going into stores. When paired with media trafficking systems, like a demand-side platform (DSP), a DMP is able to use those insights to inform personalized media campaigns and deliver messages to consumers at just the right time and on the right device. They are pretty amazing things. Of course, that amazingness can come at a hefty cost. There are plenty of vendors out there who will set up your own DMP using their infrastructure and your data; the average estimated cost to get started is around $250,000, not a small investment by any means. Maintaining and optimizing the data over time is an additional cost. Some companies do invest in building their own DMP, but with the necessary staff, servers, and security precautions, you can quickly surpass the cost to outsource. Before you feel you have been priced out of the game, I have good news. There are several companies, of which AMP Agency is one, who have gone through the hassle of setting up and populating their own DMP, allowing clients to use them for their campaigns for a fraction of the cost. AMP’s DMP is called Advantage Media MomentAware,™ and has been created by partnering with some of the largest data providers. Also through our partnerships with the largest retailers and consumer packaged goods (CPG) companies in the world and our own 2nd party (proprietary data), we are giving our clients access to informed insights . We made this investment and continue to build on this proprietary data investment because we recognized that our media products wouldn’t be cutting edge without it; in fact, the market is evolving so quickly that pretty soon media products that don’t leverage insights from a DMP will be deemed obsolete. So, I’ve told you what DMPs are and how to get access to them, but how does using one impact results? Positively, of course. Here are a few examples of ways that a DMP could be used to build and optimize campaigns: Audience identification – one of our clients in the pet insurance space had low brand awareness and came to us to help with targeting prospective customers. They had small budgets and large conversion goals, usually a deadly combo when needing to get results quickly. We built our target audience by identifying the physical locations of dog parks, veterinarians, and pet stores across the nation; using anonymized data, we were able to identify patterns in frequent visitors to all three locations. We then built out a profile on their media consumption and determined the time of day to best deliver messaging for maximum conversion. The campaign improved their click through rate by nearly 250%, reduced cost by 83%, and increased lead efficiency by 19% Target specific stores – with MomentAware, you are able to use location-based targeting to deliver media to specific store locations. Have a store that has an abundance of inventory of one item? Run a campaign in the local area to drive in-store traffic. This also can be used to exclude locations that don’t have the item in-stock, particularly during promotions. Save your valuable media dollars to only spend where shoppers can take direct action v. running a blanket campaign. Connect online actions to in-store visitors – In a recent Salesforce consumer study, 52% of Millennial shoppers said they strongly/somewhat agree that it would help them if a physical store knew about the online research they had done prior to getting to the location (e.g. wishlists, abandoned cart, etc) so they could receive better service. Using MomentAware makes that possible – it associates mobile device IDs and cookies (computer) to understand site activity and when that device is in store. Imagine that the shopper gets a relevant ad or offer with a store locator when they are browsing prior to going to the brick and mortar location. Better, more relevant experience, both online and offline. With MomentAware, it’s easy. These examples are just the tip of the iceberg when it comes to using data in creative ways to deliver personalized experiences. Using a DMP is integral to activating on data quickly and easily. Are you using a DMP? Let us know your experience in the comments.
Intel is planning to invest over $100 million in the retail industry over the next five years and at the heart of that is the Intel Responsive Retail Platform (RRP), an IoT solution that will take retail to the next era of highly efficient and personalized shopping. Through RFID, video, radio and other sensors, it will enable easy, holistic integration, help to deliver a 360-degree viewpoint of retail from the store floor through the supply chain, and deliver real-time, actionable insights. The haves (data) and the have not.
San Francisco start-up Nuna has built a cloud-computing database of the nation’s 74 million Medicaid patients and their treatment. Health data on its own — billing, diagnostic and treatment information, typically recorded in arcane, shorthand codes — is not very useful. But if it can be aggregated and analyzed economically and quickly, that data is seen as a vital ingredient in transforming health care. Data, analytics and the system-wide view
Spotify puts its vast trove of listener data to playful use in a new global out-of-home ad campaign—its largest OOH effort to date—with executions that playfully highlight some of the more bizarre user habits it noticed throughout 2016. The work began rolling out Monday. It's been weird.
Programmatic is no longer a buzzword – it’s the buzzword. And at the 2016 DataXu summit held recently in New York, programmatic was the core focus of two days of learning. With everything from campaign management to performance attribution on offer, attendees were able to design their own tracks by focusing either on programmatic platforms or trends. A full recap of the summit can be found here (https://www.dataxu.com/blog/dataxu-summit-2016-event-recap/). We at AMP have been working closely with DataXu this year to expand our team’s programmatic capabilities as well as our knowledge, a team effort that paid off during the second day of the summit. DataXu recognized AMP as having the first users to complete the DataXu Professional Certification, a multi-level digital curriculum designed to measure user proficiency not only in the DataXu platform, but also on core programmatic concepts. With programmatic ad spending projected to account for more than 80% of all digital display spending by the end of the decade, it is more important than ever that the AMP team remains ahead of the curve through partnerships with platforms like DataXu. Find further reading about the Professional Certification, please visit dataxu.com/training.
The human brain has nearly 100,000 times as many neurons as the bee brain, yet the rudiments of many of our most valued behaviours can be seen in the teeming activity of the hive. So what’s the point of all that grey matter we hold in our skulls? And how does it set us apart from other animals? Does size matter?
According to a recently published report by Forrester, six percent all US jobs will be replaced by automation over the next five years. Ten years from now, 16% of all jobs will be automated. Described as “a disruptive tidal wave,” the transformation will mostly impact transportation, logistics and customer service sectors. The bots are coming.
For the past year and a half, Liza Landsman has taken on a massive challenge: overseeing marketing, branding and analytics as chief customer officer of ecommerce startup Jet.com as it attempts to compete with the retail Goliath that is Amazon. Landsman is building the Jet brand around the promise of making shopping fun through a gamified process that aims to shake up the way people shop for everything online: from household products to books, music, appliances, electronics and groceries. We love a challenge.
Having grown quickly from a niche site providing accommodations for high profile events, Airbnb turned the hospitality and travel industry on its head and generated a great deal of press and brand recognition in the process. Since its humble beginnings, Airbnb has made no secret of its heavy use of data science to build new product offerings, improve its service and capitalize on new marketing initiatives. Here’s how they do it – and what you can learn from them. I dream of data.