With a growing number of martech platforms marketers use, it has become increasingly difficult to manage data coming from all of these platforms, and even more difficult to analyze this data....
With over 5000 martech products out there, it is clear that the use of technology in marketing has made things more complex, instead of streamlining operations and improving productivity, which was the original idea. And then, there’s the “big data” stored in all of these platforms. Simply maintaining an inventory of all tools storing any type of data has become a huge headache, not only from a technical, but also from a legal perspective.
Even if you manage to keep a close eye on the integrity and security of your data, the key question is: are you actually doing anything constructive with this data? In most cases, the answer is “no” or “not really.” Apart from the most common uses, such as basic website analytics or creating siloed advertising performance reports, in most cases companies are sitting on “data goldmines” which remain unexplored.
We get it – between keeping up with the daily tactical tasks and long-term strategic dilemmas, there’s rarely enough time to innovate through using the data at hand. With the competition getting tougher each day, it’s easy to understand that your marketing should become more data-driven, but you simply cannot grasp these amounts of data on your own. What if we told you there is another technology that can help you to achieve this? And before you say “no, not another technology” hear us out.
In a standard approach to data analysis, the steps are pretty straightforward – you download the data from the platform, make sure it’s not corrupted (or at least you try to), and send it to your favorite analytics tool for further analysis. Yes, in most cases this is just another Excel spreadsheet, but you have to start somewhere. In more advanced cases, instead to a spreadsheet file this data is sent to a data visualization solution, like Tableau, or a business intelligence tool, like Microsoft’s Power BI. And in a limited number of cases, the data is sent to a database or a data warehouse solution, where it is stored and consequently used by data analysts.
This approach is great if you want to make standardized reports, look into insights that you are already aware of, and monitor the current KPI developments. But what if you wanted to unlock the full power of your data? To find out something new? In that case, traditional analytics techniques and tools simply don’t cut it. You need something that can identify data points and anomalies autonomously, without human intervention, and make conclusions and suggestions. Yes, we are talking about the benefits brought by artificial intelligence, but in a very focused area. Many market leaders are seeing the use of AI as an opportunity, and are moving towards using tools Gartner defined as “augmented analytics”.
What is augmented analytics? It’s the way to use advanced technologies based on artificial intelligence, such as machine learning and natural language processing, to automate repetitive tasks, recognize patterns, and derive insights from the processed data. Or, to put it simpler – it’s the technology that helps you make sense out of your data, and discover insights that might be missed by using standard data analytics techniques. It has been identified by Gartner as one of the top 10 technology trends in data and analytics, and continues to be one of the aces up the sleeves of many forward-looking companies.
There are many ways how you could employ augmented analytics to optimize, accelerate and innovate your marketing efforts. We have highlighted the key 7 ones that help marketers to deliver more value, faster and more precise.
One of the most common challenges for companies, regardless of their size, is to successfully manage their overall advertising efforts. At the moment when you move away from using only Google Ads or Facebook Advertising and add other platforms to the mix, things get really messy and keeping track on what’s going on becomes really difficult. One of the first things that you need to adopt to keep your sanity is a campaign naming convention. Even the smallest error, an extra space here and a missing dash there, can totally destroy your reporting, and send you on a wild goose chase for a problem with your digital marketing campaigns and ads that doesn’t really exist.
To prevent these types of headaches, data analytics platforms equipped with augmented analytics features employ machine learning algorithms to analyze and understand your naming conventions. Once they notice that there is a campaign or an ad with a wrong name, the system flags this and you can correct the error before it becomes an actual problem.
Campaign naming is just one area where machines are very useful in analyzing large volumes of data and highlighting any inconsistencies. And while errors in campaign naming can affect only your reporting, errors in campaign settings, such as CPM values or targeting, can have a much larger effect on your campaign performance. One zero extra in your CPM setting, and you can spend your whole monthly budget in a matter of a few days. Here’s where augmented analytics can really offer a helping hand.
By constantly monitoring the current results and comparing it with historic data, system that have an anomaly detection feature can highlight any type of problems, from rising costs to decreasing performance, even technical issues with your conversion tracking or website. For example, if there’s a strong and sudden spike in your CPC values, you need to know about it and intervene as soon as possible, by optimizing the underperforming campaigns. Notice this issue with a 7-day delay, and it might be already too late to prevent a solid damage to your business. Any performance marketer will recognize immediately how much value this type of reporting brings to his daily operations and to the results of the campaigns.
Identifying trends is the other side of the anomaly detection feature. By analyzing the historic performance, predictive analytics tools proactively work on identifying trends within your data. A change of customer behavior in a certain demographic, increasing ad costs due to a new competitor, an emerging trend on a product adjacent to your product line… the possibilities are endless. By employing the trend detection feature, you can save a lot of time on trying to understand the market dynamics and effort in market research, helping you to develop new products and offerings that have the right product-market fit. And of course, support them with the right ad campaigns.
In the modern, highly competitive marketplace, adapting the offers and customizing ads to the needs of the customers is one of the key methods to increase their efficiency. You most probably already segment your customers by age, geography, the device they use, and serve them the right ads at the right time and stage of the funnel. But do you really know how these segments are performing? You maybe don’t, but your augmented analytics platform certainly does. By employing a segment analysis feature, you can easily identify highly performing (or underperforming) segments, and readjust your marketing strategy and budgets.
As the campaigns roll, the augmented analytics system learns more and more about your customers and their habits and behaviors, and is able to identify various opportunities for campaign optimizations. Locating and highlighting segments that perform great is just one of them. But what if all your campaigns are performing rather good, so it’s difficult to set a priority in the budget allocation? Luckily, the intelligent part of augmented analytics is able to not only identify outliers, but also to establish a relation between all parameters.
So, if you use multiple channels or have numerous campaigns and ad groups in a single platform, the system is able to make a comparison of their relative performance and make budget shift suggestions. By utilizing the benefits that this feature brings, you will be able to quickly reallocate budgets to the most performing campaigns, leading to an optimal campaign performance, and an increased ROAS.
Shifting budgets while the campaigns are running to achieve higher ROI is critical, but remaining within the limits of the assigned budget is sometimes even more important. Don’t worry, augmented analytics has your back even in this – by using the forecasting feature, you will be able to understand the ways your budgets are being spent, and intervene if any of the channels is using too much of it.
It takes into account all the variations from the past: weekends, seasons, spikes in demand… they are all considered before the final projection is done. This predictive analytics feature is particularly interesting for agencies, who are in charge of managing budgets of numerous clients. Keeping track of all of them is indeed a challenge, but if it’s all under control, this increases the value of the services provided by the agency. It also raises the trust between the agency and the client, as there is certainty that the budgets are being spent exactly as planned.
The final feature that we want to highlight here may seem like a kind of magic, and it is extremely useful in resolving one of the main mysteries of modern marketing – omnichannel campaign performance. By analyzing the performance of all the channels you are using, the channel mix optimization feature is able to calculate the ideal channel mix for the given budget. This allows marketers to really maximize the campaign performance across all channels without any complex calculations and tough decisions. You just need to follow the suggestions from the screen, and your ROI will improve to the levels you could’ve just dreamt about previously.
As technologies based on artificial intelligence develop further, it is certain that the benefits brought to marketers by it will grow and multiply. If you are struggling with monitoring performance, or you have maybe mastered it, but want to look a step or two forward, augmented analytics can give you answers to many questions. Sometimes even to the ones that haven’t been asked yet.