Digital Marketing Analytics can mean monitoring some basic digital metrics like visitor, page views, impressions, bounce rate etc. and using it to refine the campaign strategy – to boost these numbers. With the popularity of Search Engine Optimization & Search Engine Marketing, and agencies over-selling these, businesses should not assume that is all analytics has to offer for digital marketing.
The full potential of digital marketing analytics can be realized only if businesses are looking at the power of analytics – to predict what a customer/visitor is going to do next – and be well placed to serve them. The analytics maturity model would help businesses judge where they are in their analytics adoption journey, and whether they are data ready to move to the next level:
From a process perspective, each of the stages can be mapped to the following phases:
a. Basic: No/limited understanding of the key KPIs and no clue of what to measure
b. Developing: Fairly developed reporting process with clear knowledge of what is happening
c. Defined: Understanding of what is happening and efforts are being made to understand why something is happening through data-driven evidence
d. Advanced: Clear view of what is going to happen with predictive analytics solutions integrated into the decision making process
e. Leading: Developing tools & innovating solutions based on advanced analytics, machine learning and AI driven real-time decision systems
Here, we will talk about Predictive Analytics and how businesses could leverage predictive analytics for digital marketing and allow themselves the competitive edge.
a. Better conversions with Response Modeling
With the data and content deluge, it is easy for the targeted segment to scroll past the social media and web page content, not open emails, ignore promotions and offers being served through banner ads, paid marketing and so on. Predictive analytics comes to the rescue with propensity models or supervised learning algorithms in machine learning to predict the customers who are most receptive to a reach out, and have higher chances of intended response. This approach can provide you with two options:
1. Reaching out to the most receptive target audience with a fixed budget – therefore maximizing conversions
2. Incur minimum cost to reach a defined conversion goal
b. Campaign effectiveness and marketing mix optimization
One of the primary goals of digital marketing campaigns (or marketing campaign for that matter) is to generate leads and boost sales. But how do you measure the effectiveness of digital marketing campaigns? Digital attribution combined with market mix modeling holds the key to understand the impact of digital marketing on sales. The combination works because market mix modeling (MMM) estimates the impact of digital on sales, that can then be input into the attribution study to deep dive on what aspects of a digital advertising (e.g. website type, time of day, festival vs. always on) are most and least effective. Going further:
1. An optimization model can help businesses come up with the best marketing mix for the planned campaign (with inputs on intended spend or defined KPI e.g. Sales goal)
2. Optimal number of digital touchpoints and the best channel to reach out to a lead
c. Lead Scoring
One of the most important applications of predictive analytics in digital marketing is predictive lead scoring. Lead scoring simply means assigning a score to prospects based on assessment of interactions and transactions a prospect does with the sales rep. Sales rep and marketers most often rely on their judgment and gut to score leads.
A more scientific way to lead scoring is to deploy a predictive lead scoring algorithm. Past conversion data, along with demographics, geography, interaction and behavioral data coupled with past success tags allows us to develop a predictive lead scoring algorithm.
d. Cross Sell/Upsell
Marketing is all about generating sales opportunities. With digital marketing, it is easier to reach to a wider audience given the low cost of personalized communications – through SMS and emails. Add the fact that it is ~ 8x cheaper (average, across businesses) to sell to an existing customer than to acquire a new customer. Given the low cost of email and SMS, it is easy to fall in the trap of mass mailing and mass messaging trap. However, in the longer run, this may be counter productive – as intended audience may just block the SMSs or mark the email as spam. This is the worst situation to be in – not being able to reach customers when you genuinely need to communicate with them – not only for marketing.
Cross Sell & Upsell solutions, coupled with the right medium, frequency and time to reach have been found to generate up to 3x repeat customers.
e. Customer Segmentation, Cost of Customer Acquisition (CAC) & Lifetime Value (LTV)
Though strictly not falling under digital marketing analytics, this section focuses on some customer analytics solutions that can be leveraged for digital marketing analytics. Customer segmentation allows businesses identify the key segments e.g. higher revenue generating, more receptive to promotions. Among other things, it reduces the customer acquisition cost (CAC). Once businesses have a clear measure of CAC, predictive analytics can be used to forecast the revenue a customer is going to generate in his association with the business (known as Lifetime Value or LTV or CLTV). With the right CLTV estimates, businesses can have an understanding of the segments (and customers) who are going to be more revenue generating, and try to boost LTV for other customers.
Digital Marketing Analytics and specially, predictive analytics within digital marketing is underused. We have a strong belief in the growth of predictive analytics in digital marketing. If you have questions on how predictive analytics, or advanced analytics, machine learning and AI can help you with the business problems you are looking at, drop us a line.
Xtage Labs is an advanced analytics and machine learning based decision insights company. We work with businesses to derive insights from data, and improve the decision making processes.
Get in touch with us to find out what we can do for you.