Analytics teams focused on detecting meaningful business insights may overlook the need to effectively communicate those insights to their cross-functional partners who can use those recommendations to improve the business. Part of the DoorDash Analytics team’s success comes from its ability to communicate actionable insights to key stakeholders, not just identify and measure them. Many analytics teams that don’t emphasize communication let insights slip through the cracks when executives don’t understand recommendations or their business impact.
To combat this common problem, analytics teams need to understand the strategies used to ensure an analytics insight is not being overlooked. This can be done by employing a number of communication best practices designed to identify the business decision makers who can act on the insights and directly explaining the recommendation in a way that addresses their interests clearly and concisely with supportive analytics and visuals.
Teams that can communicate effectively using these best practices benefit from the virtuous cycle of generating good insights, where emphasizing clear communication ensures focus on finding a clear direction and being actionable. The process of articulating key insights and formulating recommendations can serve as a forcing function to make data analysis more focused and more likely to be successful in driving business impact.
Here are the best practices that the DoorDash Analytics team uses to emphasize communication, clarify our thinking, and ensure no actionable insights are overlooked.
Analytics communications best practices
While there is no silver bullet to guarantee effective communication, adhering to some best practices can help data scientists present their insights effectively and drive business impact, while getting 1% better everyday, one of our core pillars at DoorDash. The best practices laid out below describe techniques that can help a data scientist’s communication by focusing on presenting what the audience really needs to know in a way they will understand, and avoiding common communication pitfalls which may distract from the insight and related recommendations.
Use a TL;DR to clearly communicate what matters
Clearly communicating the business benefits of an analytics insight is important to capture the attention of key stakeholders so they will consider the recommendations that are supported by the data. The better analytics teams are at communicating effectively, the more time they can spend measuring insights. Part of perfecting this art of communication is ensuring that all communications capture the intended audience's attention and puts them on the path to wanting to quickly learn more.
To grab the reader’s attention and highlight an insight’s relevance to the business, we often include a TL;DR at the beginning of every analysis. The TL;DR (short for “Too Long; Didn’t Read”) is a clear, concise summary of the content (often one line) that frames key insights in the context of impact on key business metrics.
While the analytics work that produced the insight may be highly complex, key takeaways and recommendations can usually be distilled down to a few sentences. Even if the TL;DR was the analysis’ conclusion, it should still kick off communication. If writing a few sentences to summarize the key insight and why it matters to the audience is challenging for a data scientist, that should send the signal that the subject matter is not currently understood well enough to communicate with key stakeholders and should be worked on further. Overall, writing TL;DRs forces analytics professionals to define the bottom line, which in turn makes it easier for business decision makers to recognize the value of their insights and learn more.
The same logic for using TL;DRs extends to any subheadings used in presentation materials, charts, or analyses. Having clear, actionable titles gives the audience an idea of what is to come, so they will be ready to pay attention to the details. There are two tactics that can make this strategy easier to implement. First of all, avoid ambiguity and ensure that all subtitles or analysis read like the title of a newspaper article. While it might be tempting to have a slide titled “Problem”, that is much less engaging than something more specific like “The problem with declining website click-through rates.”
Additionally, lead with the recommendation instead of just the data, as that gives the audience the bottom line faster and catches their attention. For example, saying something like ”20% of first time visitors to the website do not click on an item”, is not as engaging as ”Improving item recommendation could increase first time visitor click through rate by 25%”. Overall, it's important to use high level titling and summaries to capture the audience's attention and clearly communicate the bottom line before launching into the details or evidence.
Identify your audience and speak their language
Ensuring that analytics insights improve the business means actually sharing the insights with key stakeholders who can enact a recommendation. While sharing insights with influencers may seem helpful, sharing insights with audiences that can’t enact recommendations will not directly ensure insights translate into business improvements. Being laser-focused on speaking to the right audience can increase the pace of execution significantly since working directly with decision makers speeds up the pace of making business decisions.
After identifying the audience for the new insight, tailoring communication to them will increase the likelihood that the recommendation will be convincing. In order to speak directly to the kinds of business stakeholders that will likely be the intended audience, it's important to try and understand who they are and their priorities. Typically, business decision makers are very busy with a lot of priorities competing for their attention, which is especially true in startups and fast-growing companies. Therefore, connecting the new insights and recommendations to the existing goals and objectives of the target audience is one of the easiest ways to grab and hold their attention. A brief explanation of why the insight matters, framed in terms of potential impact on the audience’s key performance metrics, is a concise way of highlighting the value and relevance of an insight to their performance success.
For example, if your insight is related to API latency and the audience is the engineering team that is in charge of that API, it would be wise to use relevant domain metrics or terminology since the audience already has the technical context needed to deeply understand the analysis. Similarly, if the audience is a finance decision maker, it would be preferable to frame the insight in the context of potential EBITDA impact, a financial metric, making the insight more clearly relevant and easily understood.
Use simple data visualizations to support written communications
When communicating data-driven insights, data visualization can be a very useful tool since a picture is worth 1,000 words. However, data visualizations should not be seen as a replacement for the written communication of insights. Even though data visualizations take a leading role in explaining insights they still require interpretation to be fully understood.
When utilizing visualizations, avoid confusing the audience. Presenting unnecessarily complex visualizations can distract from the key insight and make the overall communication of an insight less effective. This often occurs because analysts have a bias towards using the data visualization technique that helped discover the insight, which might not be the best way to communicate the insight to every audience.
For example, a correlation matrix or facet grid can be an efficient way for an analyst to explore relationships in data, but presenting a dense visualization may be confusing for business partners and distract from communicating the key insight. Even insights that were initially discovered using an advanced visualization technique can often be summarized with a simple chart or table, which will be easier for all audiences to understand.
Leverage peer review to ensure the story makes sense
Analytics peer review can be an effective tool for collecting feedback and ideas as one prepares for a broader communication. Peer review can go a long way in providing inputs on the story structure, while also helping validate numbers and statistics.
For example, remembering my first days at DoorDash, being tasked to evaluate a marketing promotion, I knew all the right metrics I should be looking at, and I went about my analysis as I would normally do. But when I saw the data, I did not have enough experience with these new data points and metrics to know if I was in the right ballpark. Leveraging peer review helped me build that confidence and complete my story.
Peer reviewing projects, sharing work, and brainstorming ideas have always been part of our Analytics team’s culture at DoorDash. The review was quick as the reviewer had a lot of experience looking at these metrics and, as such, the review added a lot of value.
Avoid extraneous trivia that distracts from the narrative
In an effort to appear data-driven, many presentations and documents include a laundry list of metrics presented without context, which have little informational value to the audience. Even summaries are sometimes inundated with numbers. Data presented without narrative can overwhelm even the most data-savvy audience and make it difficult to extract a coherent story. Any insight which is not actionable is trivia. Knowing trivia is fun but can easily turn into a distraction and fog up the general message and recommendations that should be delivered.
Extraneous data also presents the risk of audiences arriving at different conclusions despite receiving the same information. The lack of a clear narrative by the author leaves it up to the audience to make their own story from the numbers. This can result in meetings that devolve into confusion over data interpretation, rather than productive discussions and decision-making. Such communication breakdowns can often be avoided if the author takes the time to tell the story, rather than simply presenting numbers.
Leverage a structured communication strategy
A structured communication strategy goes a long way in driving alignment with the audience. Consider a three step communication strategy. The first step involves ‘telling’ the audience the subject of the talk, then actually ‘telling’ them, and then summarizing what they were just ‘told’. This communication style is most relevant for a meeting with cross-functional participants because analytics insights and recommendations can oftentimes get granular or technical, making it harder for all the stakeholders to successfully follow along. Therefore, it is important to summarize the agenda upfront and recap the conclusions at the end of the meeting.
This model gives the audience plenty of opportunities to understand the top-level topics and not get lost in the details they did not fully understand. In addition, using a framework to communicate the five W's (Who, What, Where, Why, and When) is often helpful in providing consistency to the communication, and help to put insights in context.
Continue communication until the recommended action is complete
Data scientists oftentimes, after sharing their insights, move on to other projects. This creates a disconnect between the data scientist and the team executing on those insights, leading to delays or sometimes misinterpretations, driving suboptimal results. As such, a proactive communication plan for the later stages of a project can minimize these risks.
For example, for an analysis driving actionable insights, making sure the communication channels are open with regular follow-ups can help keep track of the progress and provide for efficient execution. This regular communication can involve status updates, highlighting road blockers, answering any questions, or iterating towards an even better solution. Finally, in the end, make sure to take a moment to celebrate any wins and also take time to reflect on challenges and learnings.
Conclusion
Effective communication is a critical component of the data science toolkit, and is relevant at every stage of a data science project, but is oftentimes an area which gets overlooked. This can drive inefficiencies in the projects, misinterpretation of actionable insights, and overall can prove quite costly for a company. As such, following the seven simple suggestions above can significantly improve the impact of analysis teams, while also helping forge strong cohesive relationships with cross-functional teams.
If you are excited about becoming part of an amazing Data Science team, we are actively hiring for Data Scientists and Senior Business Intelligence Engineers, as well as several other leadership roles on the team. If you are interested in working on other areas at DoorDash, check out our careers page.
Header photo by Pavan Trikutam on Unsplash
