Those nine words are often cited as the cornerstone of decision making. Be it the field of understanding technological progress or management of performance in an organization, these words hold true for every situation. Though philosophical at the first read, when you observe organizational decision making, you find that DATA SPEAKS. Needless to say that to measure and improve in any niche, we must understand and harp on analytics derived from data and use it as a key tool, not as a second fiddle.
A properly planned and implemented analysis of critical data can help channelize marketing funds towards more impactful areas. Research from McKinsey & Company suggests that an effective integrated analytics approach can free up 15 to 20 percent of marketing spend, equating to about $200 billion worldwide. Additionally, businesses that perform best in applying measurement techniques, are able to reallocate as much as 80 percent of their budgets during campaigns.
However, we must be wary of what is called an - Option Dilemma. With so many analytic tools available, we must try to select the ones with the best artificial intelligence and which provides the most relevant insights and then use these insights to gain optimum leverage on planning and decision.
At Lilypads, we define marketing analysis as the comprehensive measurement and optimization
of marketing practices through relevant metrics and data sources. The extent of the information which is relevant
and which needs to be filtered from the huge information pool, continues to grow it's boundary, thus making it
even more difficult for marketers to decide the exact set of information to select and focus on.
The rule of the thumb to identify the metrics which is most relevant is the one which speaks the most on how the marketing
investment relates to the consumer's journey - from acquisition to close.
In other words, itís about removing guesswork.
The conversion cycle - which was the cycle from inducing awareness in customers to subtly guiding them to consideration and then to decide to purchase, was relatively simple and straight forward unless the dawn of the digital age.
Now, this is ever so more complex - with the cycle having transformed to a multi-dimensional hexagon, given the diverse and different ways in which consumers find, relate, consume and use information.
A prospective customer may discover the brand by accident or may be introduced to it by word of mouth or serendipity. They will then chance upon that brand in other digital posts like blogs or video ads or social media posts and shares. Eventually, a well-planned PPC ad or a remarketing campaign may convert the prospect to a customer. However, this is but simply put. A hoard of other factors like emails, banner ads (conventional) etc, play their important role into the conversion cycle.
All these conversion factors, irrespective of their percentage of influence, do leave a digital signature on the process. While looking back at the data to weigh the criticality of each of these processes, it becomes highly challenging to figure out the relevance of each of these.
However, as the challenges scale up, so does the ability of the analytic tools we use to better track and analyse the data.
One of the important methods for connecting the dots is Marketing Attribution. It refers to the process of marking
a set of user actions that drive a specific revenue outcome. Assigning specific values to these user actions is critical to
understand what the fine line on one side of which conversion occurs and the other side sees attrition.
But how do we understand which campaigns and lead generation processes are most impactful for conversion?
Leveraging on artificial intelligence based on business decisions, we can focus on three different attribution models which help us with the answer.
How did a customer first interact with your brand?
Did he click on a paid search ad?
Did she fill up and submit and inquiry or feedback form?
First-touch attribution assigns revenue credit for the deal to that initial point of contact.
Of course, it is rare that a prospect becomes a customer solely because of that singular first interaction. Good marketing practice based on data models, involve nurturing the leads through different channels and practices. The influence of multi-touch attribution can be linked to each step of the conversion journey. This is usually achieved with the help of a linear model where an equal weightage is assigned to each touch point that has a higher influence over its other data sets.
Contrary to the first-touch attribution, the credit for conversion of a lead to an opportunity, happens to be the final touch point of the process. There is an inherent appeal to this process as it helps better recognize the steps that trigger a decision. But by itself, it should just share a part of the closure, not the primary deciding criterion.
In spite of the choices, each of the three attribution models as discussed, can be a right fit for specific situations. By using them in conjunction with metrics such as velocity, which measures how quickly a campaign moves prospects through the pipeline and others that quantify resource allocation (Cost per Order, Cost per Deal), we can develop a more precise understanding of ROI and how to optimize it.
There are two fundamental principles to keep in mind at a high level when thinking about marketing analytics:
This means being proactive rather than being reactive. While using a marketing analytic tool, it is important to define specific goals during the early phase and then to compliment it with a detailed plan of how these goals will be achieved. Align your CRM, marketing automation software or attribution engine with these objectives and continually track progress throughout the life of a campaign..
It may so happen that some members of your marketing is struggling with the more specific points. If so, it is always worth the time to guide them and help them gain a full understanding. This can be achieved by using both data and metrics from market research as the core of every program, right from conception to the field execution. In the modern marketing world, this is imperative. In an age where data speaks, if you cannot measure and quantify it, you won‚Äôt be able to prove it either. Your competitors are certainly doing both of it.
To better design your marketing campaigns and contents, we must understand the contents, the messages and the call to actions which captivate the user's interest, holds their attention and motivates them to take the next step. Even if the results are not conducive to the desired outcome, we must be well informed of what doesn't work so well and why. A zoomed in examination of the first-touch attribution parameters and the indexed velocity matrices are the starting point towards this.
The approach should be more empirical than opinionated. In a smart and effective marketing system, opinions do not count but data points and informed prediction does. It is important to realize that these predictions are not eternal. A context or an approach which was relevant to a niche customer group at one point of time may lose its relevance in the current context. As interests and affordability are continuously changing, so our approach needs to be more dynamic and review and predictions more frequent to keep up with the latest and most relevant tipping points. This is why predictive analysis and modelling is beginning to hold more value and is viewed as more relevant.
One of the best uses of attribution models is not just to get an overview on the performance of your content but to monitor the user's engagement at each level. Once we have a better understanding of which of our content works and which doesn't, it just remains to follow the style and approach of the content that works and weed out the ones that doesn't. Lending a good ear to the marketing analysis brings us closer to our customer and helps us understand them better. Once we do, we can communicate in a more effective way with them and convey information which is useful, engaging, relevant and with a higher chance of conversion.
Many teams track the lead generations from different referral and affiliate sites. But these seldom provide strategic information. The missing link is that these teams only focus on lead generation, but the tracking goes cold further into the pipeline. Deep data marketing based on a varied data pool, dive bridges this gap to give crucial information on not just the incoming leads, but also on their performance.
Another important criteria which usually gets overlooked during analysis is the influence of certain sources to affect purchase decisions. The more common focus of most marketing teams while reviewing customer journey reports, are at the top and bottom of the conversion funnels, keeping the middle starkly neglected. Interestingly, this section has a huge effect on the path taken by the user - whether they bounce off or take another step towards conversion.
This is why it is mission critical to not just identify the entry point, exit point and conversion point but also to track the entire path of the user journey. An effective marketing team will leverage on the analysis to track where a prospect enters the funnel, which path they take and why and what influences their bounce-off or decision to purchase.
By culling data from a multi-touch attribution model, we can essentially draw out a map that tells us how prospects move through the funnel, and where opportunities exist to engage with a message that works.
A successful strategy for a relevant analysis is usually one that provides the right approach at the right time. In many marketing campaigns which do not live up to their potential, the failure is usually not bad approach, but more so an in correct chronological placement. A good analysis should give data on the entire user journey and correlate the responses to each parameter with the time, both at the individual user level as well as at the collective group level.
Equipped with this information and well framed strategy, you can plan to place each element at specific positions of the pipeline. Which element to place and where to place it will be determined by allying with the data driven analysis. Such an approach will have the optimized chances of increasing velocity as it will be data dependent, hence predictive rather than being hypothetical and wishful.
Attribution models + defined metrics = Measurable ROI
Understand and plan optimized content placement in the funnel.
Define specific goals and separate good data from data noise.
Leverage on analytics to plan the what, why and when of the marketing plan.