This guest post was written by Irina Kovalenko from SmartyAds.
When we talk about developing an operational marketing strategy, no matter what product or service we deal with, the thing that matters the most is data. Using the knowledge of whom you sell to, how your consumers behave, and why they need your product is the only way to make them a genuine and relevant offer.
The relevance of your marketing strategy will depend on how much effort you put into data collection, segmentation, and processing – in other words, how hard you try to understand the needs of your customers.
According to a global study of data-driven marketing from DMA/IAB Data-Centric Organization 2018, more than 90% of respondents consider data collection to be one of the most important factors that increase the effectiveness of segmentation and predictive analytics during a marketing campaign. In 2019, the same share of “data-centric” companies make the focus on audience data as their most important asset for building future enterprise strategy.
Why is the world going data-centric?
Marketing experts recognize in data-centric marketing approach a new opportunity and challenge at the same time. The creation and support of internal analytics system, as a whole, boosts the core marketing performance, ROI, and helps run a robust content marketing analysis. As we witness the birth of new data analytics decisions every day, separating the wheat from the chaff becomes harder. The growth of data analytics solutions is related to the rapid increase in demand. Still, choosing the right option appears to be a tough task.
Since customers massively moved online, so now have offline businesses. At the same time, the talent gap remains to be number one challenge that companies face turning data-centric. The speed of technological evolution simply outruns the ability of the market to adapt and train skillful professionals in this area.
The GDPR and high-profile data-breaches are also the reasons that push companies to go on a wild goose chase looking for a all-in-one platform or third-party service that would manage the user data for them. Like everything else on Earth, each of these approaches has advantages and disadvantages.
Data management 2019: Cook at home or go to a restaurant?
Even data management can be compared to cooking. To put it simply, you either make it on your own and spend a lot of time sweating over raw products preparation, or outsource the task to the restaurant and spend a whole lot of money there. Whatever your choice is, with data management software we have to deal with slightly more complicated issues than choosing between time and money.
Depending on industry, each brand may need to use a specific type of information that will bear more relevance to the business. For instance, in post GDPR period many advertising brands decided to abstain from using third-party data in order to create transparent and trusted relationships with their customers. That’s why it is important to understand what type of data you’ll be using and what for.
To begin with, it is important to understand the difference between third-party data and the one that belongs to your brand (first-party-data).
- Your own data (first-party data) is the brand’s own type of user data that is collected in the process of consumer interaction with the website: making purchases, filling out forms and questionnaires, leaving personal contacts, etc.
- Marketing campaigns data (second-party data) is a type of information about marketing campaign results and user activity that includes behavioral factors. In fact, this type of data is similar to first-party data, but it requires a greater number of stages and interacting channels to collect it.
- Third-party service data (third-party data) is information that is gathered and provided by third-party services: third-party websites, payment systems, resources designed for email newsletters. This kind of data is aggregated by external platforms. Figuratively speaking, it’s their product that they sell to businesses.
Currently, it is third-party data that serves as the basis for the digital advertising ecosystem. While launching a programmatic advertising campaign on a demand-side platform, the brand needs this data in order to channel a particular type of ad to a specific user at the right time and screen.
Getting access to the 3rd party data, the company reaches previously unavailable audiences because the purchasing coverage area is not limited to website visitors only. There’s no doubt that 3rd party data brings tremendous marketing benefits, but a well-developed strategy based on first-party data is also an excellent way to engage the customers who are the most loyal to your brand.
As a data owner, you will always know exactly which source your data originated from, and that it hasn’t expired till the moment you need to use it. Gathering and processing your own data is also a guarantee that you won’t overspend purchasing it from outside.
A significant share of those companies who turn data-centric, are searching for predictive analytics and cross-channel measurement in the data analytics software.
If you are among those companies, you must know that adopting a new data analytics software requires revising your individual business needs in order to scale up the solution.
Developing a marketing strategy, many experts today prefer to focus on
But how should each type of data be used and when? In order to let the data types complement each other, you’ll need to develop a strategy that makes the most out of your marketing campaigns. For this, we have prepared four steps that you need to consider.
1. Define your target audience using first-party data
The first stage of your marketing campaign planning may include customer data collection through your own resources. This will be the freshest first-party data that includes:
- the customer’s first name
- last name
- email address
- activity on the website
- any other information type that can be collected and stored in your CRM system
Such information might come handy, whether it is the history of purchases you will be analyzing in the future, or defining the preferences to particular product categories.
Based on such data you as a multi-channel, for instance, can understand that “Gen Z“ is the segment that outruns your all other segments in the eCommerce niche. Knowing this, you can start a display campaign that targets “Gen Z“ customers based on their first-party data.
In case your product is highly specific, you might need to narrow the targeting to take into account tastes, lifestyles, behaviors or preferences of your target audience. For this, you’ll either need to find a demand-side platform with behavior targeting options or integrate it with external DMP.
2. Target your audience with second-party and third-party data
Not all of your customers are the same, so naturally, they’ll react differently to your product or service. In order to start targeting your audience more effectively, you need to link the sources of second-party and third-party data with the customer’s buying behavior, which can be done automatically, if you use a DMP platform. This way, you can also begin to segment the customer base into mutually exclusive groups.
What does it give to you? Using the second-party data, you will get an idea of the behavior of your customers on the Internet (visits to the site, clicks, views of specific pages, time on the site and more). Third-party data will enable you to add demographic and important behavioral data to the profiles of your customers.
Based on a variety of information you can create an accurate portrait of your client for effective audience targeting. Thus, for instance, the multi-channel retailer inspects the purchasing history and finds out that their fastest growing segment is “Gen Z“ audience. At this point, a corresponding third-party data to the profile of each customer can be added to segment the audience according to the channels they prefer for information consumption: mobile, laptop, etc.
Thereafter, the portrait of each “Gen Z“ customer is linked with their shopping history. Exactly from such a holistic view of this category of users, the retailer might find out that “Gen Z“ indeed tends to spend much less money during their online shopping experience. Since technology will also gather multi-channel information, the retailer can find out, that this audience prefers Facebook rather than corporate website.
By the way, according to Adobe content consumption research, social networks, as well as connected TV are the most precious mediums where Millenials and “Gen Z“ spend most of their time. For the brands that regularly optimize their creatives and suit them according to the channels, these mediums have become the gold mines that generate the best incomes and drive customer acquisition.
3. Interpret what the data says
Now that the audience is neatly divided into segments, you can say that you understand your audience better. Start developing a strategy based on the information you’ve gathered and segmented with your technology.
Glancing at the data delivered by your data management platform, you can think of the best ways to reach them with your offers, messages or advertisements. In our case, the retailer can start targeting the “Gen Z“ audience using personalized offers through a demand-side platform that enables sending different types of creatives to different segments and through various channels. Based on segmentation, the retailer can also personalize the onboarding experience for different kinds of customers.
With this knowledge, you can conduct personalized advertising campaigns via real-time bidding protocol through all Internet-connected channels: tablet, mobiles, desktop, and even connected TV. Targeted advertising is highly relevant, it appeals to the tastes and interests of your customers and increases advertising results in the end.
For example, you know that your user has recently bought a phone from your store. Now you can feature the cases for phones on expandable media banners. In fact, you can even put the phone on the banner picture to make a more impressive accessory comparison for the customer.
4. Analyze, target, repeat
When your online advertising campaign is over, and the results can be analyzed, you’ll need to go back to the first step and repeat the whole process again. Measure the ROI indicators, start from the initial data that you had, and study the customers who performed the conversion action.
Upon completion of the advertising campaign, you will be able to assess the impact of your data processing strategy accurately and define if you were addressing the audience segments correctly. On a demand-side platform, such analysis can be generated in seconds. All that you need to do is to pick the period and the performance indicators you’re curious about -eCPM, impressions, clicks, installs, etc., and click to download your report in real-time.
5. Combine technologies
In the first step, we’ve mentioned using CRM and DMP, but the practice shows that more than half of small enterprises process the information for the reports manually. Those of you who once tried using CRM, know there’s no turning back to slow and mundane manual data processing, because AI and MA algorithms are more efficient than humans, by any means.
Let’s find out if you can get by using a single CRM system. CRM solutions can work with personalized data and collect all customer first-party data. The DMP, meanwhile, can work with various types of data and integrate with other tech solutions, like demand-side platforms, to send the message through the right channel.
The “symbiosis” of DMP and CRM can become a competitive advantage for brands due to previously unprecedented audience reach. DMP can be quite effective alone, however, when it comes to analyzing live sales and transactions at the points of purchase, CRM platforms are irreplaceable.
On the other hand, when this customer data is transmitted to the DMP, you can avoid the situation when users are harassed by an offer with the product they have already purchased from you. A complete look
Your second greatest advantage is a chance for additional segmentation. Inside the groups, you can define other subgroups for your campaigns. For example, select “all cookies of users whose average check exceeds $50.” As a result, you can make more targeted offers for the selected subgroups, which will undoubtedly cause an increased response.
Instead of conclusion
User data has always been there, but only now we’ve learned to apply it on such a global scale, mainly due to data processing technologies like data management platforms or CRMs. Shifting the focus to the richer and better data collection involves using all types of customer data because they can complement each other.
The combination of user data is what helps the brands develop a unique operational process, achieve higher ROI from advertising, and significantly enhance engagement and user loyalty.
Irina Kovalenko holds the position of CMO at SmartyAds programmatic advertising company. The company consults in developing advanced and innovative programmatic software for media buying and selling automatization solutions. Irina has robust and experienced knowledge on the programmatic advertising, so she elaborates and executes new marketing strategies and shifts for the SmartyAds.