Did you know 97% of U.S. Companies feel driven to turn data into insight? That means most of those companies look to data to understand their customer needs, find new clients, and increase value. However, 92% of companies think they have a data quality issue. Knowing that you have a (data) problem is the first step to finding a solution.
Here are some key takeaways from Gartner Magic Quadrant Challenger, Experian’s, whitepaper on data quality in 2015:
- Reduce human error. Identify where information is exposed to this error and find technology that is lowers this risk.
- Appoint a central data owner and invest in staff. Someone to handle all aspects of data and is an expert.
- Conduct audits. Be proactive and look for common errors, maybe even invest in detection software.
- Make it an organizational concern. Use tools consistently across departments and ensure data is complete as collected.
- Track metrics that are beneficial. Make a case for data quality by tracking key metrics impacted by data and how those affect business outcomes.
Let’s back up a little bit from last week’s tips on initial steps towards data quality and talk about why companies should care. Based on Experian’s report, as marketers continue to automate processes and do so within much tighter timeframes to keep up with the consumer, maintaining and analyzing accurate data will continue to be a critical component. Top drivers are: understanding customer needs, finding new customers, increasing value of each customer, and securing future budgets.
Here are some trending reasons on why companies care about data quality in 2015:
- Increase marketing program efficiency, enhance customer satisfaction, and enable for more informed decisions
- 87% of companies are using predictive analytics* across their business in one way or another and those who use it have significantly increased profits in the last 12 months, this requires clean data to work properly
- Customer Experience Management (CEM) is a hot topic for companies today, an increasingly important part of this management is done through data because you need data to develop better personalization
*Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns (Source Wikipedia)
As a marketer, you want to always be improving the customer experience so they keep coming back. One particularly popular topic for companies right now is what it takes to have a well-oiled Customer Experience Management (CEM) operation. How are successful companies leveraging data to improve this experience and be profitable?
For starters, they have a more sophisticated data management strategy. Disorganized and low quality data is simply expensive background noise. Actually investing in some tools to mitigate your data integrity issues would be a great step in the right direction. In comes progressive profiling.
Salesforce’s Pardot explains progressive profiling as the automation of displaying new form fields to prospects based on data points previously collected. By using conditional fields, you can use less form fields to maintain high conversion rates and still capture new information.
As you think about your marketing mix investments, you will realize that your B2B customers are going to interact with multiple channels prior to adding value to business. Someone may do Google search on marketing services and find your marketing agency, then go to Facebook to further research your agency, then visit your website directly, and finally sign-up for services. This is called a “search session.”
It’s great that there are so many touchpoints guiding the B2B customer to the finishline, but which channel interaction gets credit for the conversion? This is both the definition and reason for Attribution. We need Attribution Modeling to find out how much credit each touchpoint gets before conversion. This enables efficient budgeting, to find out what is working and what isn’t, and accurate ROMI calculation.
What Attribution Modeling is NOT is a “set it and forget it” practice. You cannot build a model and walk away because the industry, your data, and your business objectives will evolve. You shouldn’t make things too complex right out of the gate. Its human nature to want to overcomplicate things, but only through active monitoring of data over time will you really be able to experiment with what’s best for your business.
Remember when we talked about “search sessions” last week and the customer journey with all those fun clicks before conversion? You might still be scratching your head wondering which one gets credited for the conversion, but we’ll review a few ways this can go down.
First up we have Last-Click. Not to be confused with the order of introduction, but this model gives credit to the last Channel before conversion, First-Click models give credit to the first Channel, and Linear models give equal credit to all Channels. These are default models in many platforms that offer Attribution Modeling (like WebTrends or Google Analytics). Read about more models’ pros/cons here and find out why Time Decay modeling is your friend. Custom Modeling is also great if you have a stellar data measurement strategy!
Some honest thoughts shared with influencers in the space: Last-Click and First-Click models are kind of lazy and unfair. You cannot derive much value from them. Why? They skimp out on giving credit to your Channel efforts in every other step before conversion. This will skew your ROI reporting and mess up your budgets. We can go on with this topic, but let’s regroup next week.
One of the key necessities for Attribution Modeling is successful analytics tracking and correctly tagging all your ads and campaigns. You need to have an appropriate measurement framework for Attribution to work in your favor. Why? It all relies on… wait for it… GOOD DATA!
There are four high-level concepts of analytics tracking that you need to understand before getting started:
Campaign Tracking – In which campaigns are your customers interacting with you? You will need to set-up tracking links to collect this data.
- Value Event – Actions that do not tie directly to a conversion, e.g. someone watching a video. Set-up Goals within your web analytics platform for tracking.
- Conversion Event – Tied directly to a baseline value-add on a website, i.e. form completion. These are also called micro-conversions and can be tracked by Goals.
- PII (Personally Identifiable Information) – Try building a framework that collects PII so you can attribute success at an individual level and optimize/personalize content along the journey.
See the diagram for an example of this framework. It is a visual way to see the purpose of every webpage, event, and action within any view. It tells you what value events you want as a result of these campaign investments. These are the building blocks for proper Attribution Modeling.
To find out whether or not you have an attribution problem, the first thing you can do is look at your Path Length report in your web analytics platform. If a significant percent of your conversions have greater than one path length (aka more than one channel they pass through during their journey to conversion), you might have an attribution problem.
To dig a little deeper, what you should do next is check out your “assisted conversions” report. In this report, look at the credit a model gives each channel, i.e. Last-Click. If value<1 then Last-Click worked for that channel and if value>1 that channel is not getting much/any credit with that model. Once you do this exercise you will really start to see how blindly picking an attribution model can hurt your company’s marketing budget.
This exercise is simply a diagnostic test on your attribution health. It should be noted that there are simply too many paths a customer takes to actually control the path of every potential customer. If you have time, you should definitely read this blog post by web analytics guru Avinash Kaushik to read more on attribution issues. We will probably reference it a few more times.