How can you align data reporting with your customer journey?

My number one tip for reporting (on almost anything) is: don't overcomplicate it. This is especially true for analyzing the customer journey. There is no single, universal, linear journey that every customer is on. If you make your report too complex, it will be hard to build and maintain and likely won't be used. Instead, think about the key moments in the customer journey that are both a) trackable and b) significant for your business. Collect clean, consistent data on those milestones. And establish solid routines for evaluating and acting on the data. This will be far better than an overly complex report that no one reads.

Make sure you know the company mission so you can properly prepare goals before starting customer journey. You must flow out multiple scenarios from good to bad customer journeys. For instance, what happens if the customer deviates from your intended path? how can you troubleshoot and capture pain points. How do you get customer back to the desired path?

One suggestion that I have when defining the customer journey is to take an "outside in" view vs. an inside out. We often map the journey by looking internally at our processes, systems, goals, etc. Although necessary to do this, don't make the mistake of starting there. Instead, start with the customer's experience (what they want and what you want for them), then align your internal mappings to that ideal experience.

In mapping out Customer Journeys and reporting, I always put myself in the place of the customer. What would be my ideal customer journey? What kind of clean reporting would I like to see? How would I like it communicated to me? How often would I engage with these reports? It's especially important to check-in on what feedback or improvements would the customer like going forward - constantly evolve your reporting - stay agile.

The key is identifying the meaningful interactions. Many times in Marketing Operations we are asked to drill down to the the smallest level to figure out the journey, which many times is equated to attribution. This can get over complicated and over engineered. Thankfully, we now have intent tools that utilize machine learning and AI to figure out the journey for us. The key though is to not try to retro fit that data as it stands in those platforms and how they are reported into how we defined attribution or customer journey even 5 years ago.

Data is the bedrock upon which we construct marketing campaigns. However, the ongoing challenge of ensuring timely data availability and accuracy for marketing reporting persists. With multiple business functions influencing the customer journey, we find ourselves dealing with an array of systems and technologies capturing customer interactions. Connecting these silos and fragmented data sources within our organizations is a resource-intensive and costly endeavor. Moreover, it is difficult to quantify the ROI of the efforts.

Data is my passion. It's important to choose the right CRM or tech stack that compliments each other in all departments. And It's just as important to map out what your end goal and reports needed in each section (sales, marketing, ELT, etc). More times than not, we quickly decide on data sources, build a generic data flow systems and add on later. This causes an ad hoc system with inconsistent and inaccurate reporting and analysis. Before deciding on data sources - Map Out: -Each stakeholder / department goals -What each dept wants to track - All reports needed -List out needs vs nice to haves and make collaborative decisions with stakeholders When you have what the end game needs to look like, decide on data sources, process, etc.

Another important segment is defining the different channel from which you drive deals. Ideally a first touch attribution model plays a healthy role here to understand which source is actually bringing in most leads. Lets be honest, you would not like to burn $$$$ on paid ads on platforms like Instagram or Facebook if your audience is just not present there.

When it comes to data, it is easy to get overwhelmed by thinking that every single piece of data is important. Instead of gathering, cleansing, and organizing every single data point, I would recommend that you take a proactive approach in identifying the data that you need -- and then identifying the best source to obtain that data and the best processes to manage that data.

When considering data sources, my primary focus is on consolidation so that I can extract insights and make informed decisions.

The metrics that you choose depend solely on what you wish to achieve with your strategies. Define your goals and objectives before you select your metrics.

It is important to choose the right metrics of the customer life-cycle. To do this you need to assess the priority objectives for each stage of the life-cycle. For example, the goal for early stage adopters of a product could be moving them to being Primary Users. The metric here could be the frequency of usage or number of transactions over time.

Marketing is no longer just about generating leads. Marketing has a direct infusion on the revenue. So it’s important to have a clear list of KPIs and metrics you want to measure with respect to marketing data. Some of the basic ones are: - revenue influenced - revenue closed - renewals - leads generated - quality of leads - speed to lead

Similar to making the mistake of focusing on gathering too much data, defining the metrics that will actually measure performance against your goals is important. Remember that if you are measuring everything, then you are measuring nothing. :-) You would much rather measure the key metrics, then expand into the deeper analytics as needed vs. trying to analyze or look at everything all at once.

When collaborating with start-ups and small businesses, I make sure to carefully evaluate the implications behind the metrics I present to my clients. This is particularly important when working with limited resources such as time and money. Additionally, I always keep in mind the distinction between correlation and causation. For instance, I ask myself whether an uptick in engagement actually leads to a boost in retention or awareness.

Reports are mere charts if you cannot tell a story from it. Build a dashboard that tells and showcase the story of success/failure of the marketing activities. For this, I generally divide my Marketing Dashboard in the following sections: Lead Generation Revenue influnced Attribution Email Marketing performance Paid ads performance Website analytics

We all like a "pretty report" but I would recommend that you focus on telling the story. Make sure that your "reports" are actually insights that tell the "why" and the "so what" to help the business understand what is happening, but also make decisions on what's next. Next.Best.Action.

Controversial take, before building that complicated dashboard in Lookr or Tableau, start in Hubspot reports, or even...EXCEL. Why? Because often times you learn something new when you run a report, or hear from a colleague about a different kind of view they want to see. This means if you take your first draft and spend expensive development time building a new fancy report in a fancy tool, it could be completely wasted when your supervisor or the data points you in a different direction. Instead, by starting in a low-time-cost tool like native reporting/Excel, you can learn the data, what it says, what is possible, and what it'll take without actually investing the time to build it. This will make a robust v2 worth the time to build!

When it comes to data visualization, it's not enough for it to just be informative - it also needs to be actionable. It's important to ask questions like why a 2.1% open rate matters and whether we're comparing it to our previous email. We also need to consider if the previous email is even relevant to the current one. It's important to tell the story behind the numbers so we can truly understand their significance.

Of course we want to develop the summary of what the customer journey is all about. It's also important to develop multiple levels to it. It's super important to understand, there isn't just one story to tell. Go in deep into the data to ensure data integrity, the data is clean, the data fields are full (not empty). If you are missing data and developing summaries, you aren't telling the full story. So, you could be making the wrong assumptions. Clean you data first. If you have to manually add in industries and company sizes, do it. Develop summaries with all information before sharing it with others. Develop multiple layers - reporting on customer journey depending on job titles, industries, company size, location, etc.

Always tell the "why", the "so what", and the "what's next" when looking at your data insights. You want to understand the story of what's happening and how things are tracking, but you also want to understand why things are progressing (or not) to be able to pivot, adjust, or lean in.

When analyzing your statistical insights, it's important to consider your assumptions and how they affect the recommended actions. To get a more nuanced understanding of the customer journey, it can be helpful to involve team members or stakeholders who have firsthand knowledge and experience (AKA boots on the ground). This way, you can capture more qualitative insights that might be missed by looking solely at the data.

A reminder that data insights aren't something you do once and move on. Instead, it's a continual learning process. So how do you ensure you are revisiting your data? A few ways: 1) Don't oversaturate yourself with data. Instead, pick the key reports and check those regularly 2) Make the reports easy to find. If you often look at a report 3 different ways, have 3 different reports built out and on a single dashboard to save clicks 3) Build reminders and habits. If it's a daily indicator, start your day by looking at it. If it's weekly, check on Monday. If it's monthly, set a calendar reminder for the last Friday of each month Simply put, constantly come back to your key reports to inform current business practices and future changes!

Think about the different steps that your customers take from the moment they discover your business until they become your advocates. What are the patterns, trends, and insights that show how your customers move through the journey and what influences their choices? For example, you may use customer journey analytics to segment your customers based on their needs, preferences, and behaviors, and to identify the key touchpoints and moments of truth that affect their experience. you may use the data to test and refine your strategies, tactics, and content for each stage, to personalize your interactions with your customers based on their needs and preferences, and to align your teams and resources around your customer goals.