INBOX INSIGHTS, November 2, 2022: User Stories and Requirements Gathering, Direct Traffic

INBOX INSIGHTS: User Stories and Requirements Gathering, Direct Traffic (11/2) :: View in browser

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Creating User Stories to Gather Requirements

You don’t need yet one more opinion on what’s happening on Twitter, right? I didn’t think so. In that case, I’m going to talk about how we are creating user stories to gather our own requirements.

I’ve been struggling to get a handle on my sales data. We have a CRM but because we’re a small business I cannot afford the tier that would allow me to do any kind of custom reporting. Our CRM allows for basic functionality at the moment, such as collecting prospect data and tracking deals. I’ve been scratching my head, trying to figure out a solution to this. These are some of the options I considered.

Do I switch CRMs? That’s a pain in the butt for a couple of reasons. First, I would need to evaluate my needs and write up my requirements. Next, I would need to evaluate the different CRM options. This might mean talking to other salespeople, sitting through demos, and all that good stuff that I can’t focus on right now. Last, I would need to create a migration plan for all my data and an official rollout for the new processes for data collection on the new platform. Ok, so that’s not an ideal solution.

Maybe I can download the data and merge the different tables using Excel? I would need to first make sure that all the different tables in the CRM are connected with some kind of ID. Then I would need to develop a process for downloading the data, cleaning it, and then merging it. Then I need to create a static report and make some decisions. Another not ideal solution since this one would be time-consuming and error-prone.

After talking to Chris, we determined that he could extract data from the CRM’s API, bring it into Big Query, and build reports in Looker Studio (formerly Google Data Studio). Automated data extraction and reports? That could work. First, I would need to figure out what I need before Chris starts extracting the hundreds of data points.

How did we proceed, you ask? If you guessed user stories, you get the gold star for today!

Chris and I hopped on a video call last Friday and talked through about 25 different user stories. Why so many? Because I had different questions that I wanted to answer. Going through this exercise did a couple of things. We were able to narrow down the data points that we needed to extract from the CRM. We were able to focus the purpose of each of the reports we would build. We were able to determine which tables we needed data from. We were able to see where there was an overlap between user stories.

Fun fact, a single user story hits all the 5Ps.

As a [persona], I [want to], so [that].

  • The [persona] informs “people”.
  • The [want to] informs “process” and “platform”.
  • The [that] information “purpose” and “performance”.

If done correctly and thoughtfully, a single user story can tell you just about everything you need to know to move a project forward.

Here is an example of some of my user stories for building reports with our sales data:

  • [1] As the CEO, I want to get a weekly sales report containing deal age, so that I know where opportunities stand.
  • [2] As the CEO, I want to know what services we’re selling to most, so that I can hire contractors for those services.
  • [3] As the CEO, I want to know the source of a deal, so that I can align our efforts with our digital marketing activities.
  • [4] As the CEO, I want to know when a deal in the pipeline was last contacted, so that I can work with biz dev on outreach.

The user stories are not overcomplicated and the goal is that each one was a single purpose.

What’s next? Chris will write code to extract the necessary data points into Big Query. Once we extract the data on a regular basis using automation, we can build the reports in Looker Studio. I’ve requested that each user story is on its own page in a book of reports, listed at the top so that it’s crystal clear what question is being answered. In my view, it’s ok if there are redundant tables of data. Each user story is its own question and when I am looking for the answer to that question, I can easily find it. Honestly, I can’t wait to start using the data to make decisions.

Are you using user stories to gather your requirements?

Come tell me about it in our free Slack group, Analytics for Marketers »

– Katie Robbert, CEO

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Binge Watch and Listen

In this week’s episode, Katie and Chris tackle news media’s usage of data. Is what you read believable? How would you go about proving it? We examine some recent claims in Bloomberg and Business Insider about racial slurs on Twitter and the process for verifying that claim, extending it to the role data-savvy organizations should play in ESG and social good. Tune in to learn more!

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Data Diaries: Interesting Data We Found

In this week’s Data Diaries, let’s tackle something very interesting – the rise of direct traffic. One of the privileges of having clients in many different industries and sizes is that we can look at trends across websites to see if something is affecting just one site or if there’s something bigger at play.

As we were putting together client reporting this month, we noticed something unusual: the amount of direct traffic (meaning traffic that lacks attribution data) seemed to be on the rise.

We were unsure whether this was the case or not, so how would we determine it? We could start by looking at sites’ ingestion of direct traffic:

Direct traffic

While an easy answer, it’s not a correct one; direct traffic is inherently part of the website as a whole, so if a site lost traffic during the study period, direct traffic would go down as well. What we need is an understanding of the percentage of direct traffic as a whole for the site.

So, let’s take a look at these same sites, but aggregated and anonymized. What do we see as a percentage of direct traffic?

Percentage of direct traffic

What we see over the last 4 months is a significant increase in the amount of direct traffic as a percentage of the sites’ total traffic. This is meaningful because even if a site lost traffic during the study period, direct traffic as a percentage of the whole increased.

For marketers, this is bad news. Direct traffic is traffic that has no attribution. We don’t know where it came from, and it could come from a variety of places – misconfigured ads, emails missing tracking codes, people just typing URLs into browser – the possibilities are limitless.

What should we do about it? The rise of direct traffic overall among this selection of sites from different industries seems to indicate it’s not limited to one vertical or type of customer. It might be a change in browsing behavior or device privacy settings.

What we should do as marketers is redouble our efforts to vigorously use tracking parameters so that we know where people are clicking from. These are UTM tracking codes for Google Analytics, and cid codes for Adobe Analytics. Never let a URL go out in a piece of marketing content (emails, social, ads, etc.) without tracking codes appended – and if you need to make it look nice, use a link shortener to not only tidy its appearance, but preserve your tracking codes.

We will never get rid of direct traffic entirely, but there’s no reason it should ever be more than a small portion of our site’s traffic if we’re vigilant about analytics operations and governance.

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