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Building a data-driven company starts with having a data analytics strategy.
An effective data analytics strategy relies on three key components: people, processes, and data infrastructure.
This ensures that your analytics strategy is actionable and accessible to everyone who needs it to make decisions.
In this post, we’re sharing 12 tips to help you develop an effective data analytics strategy.
Almost 90% of the marketers and data scientists we surveyed said that a well-planned analytics strategy was very important.
So, it is no wonder that 97.4% of the people we surveyed had an analytics strategy.
Having a data analytics strategy wasn’t isolated to one industry or type of business. It was applicable to everyone from marketing agencies and SaaS businesses to healthcare, ecommerce, and education.
While there is a consensus on having a data analytics strategy, who actually creates and implements it can vary from a central analytics team to individual departments and outsourcing it to a freelancer or agency.
In fact, Kevin Miller explains, “There are two main components to any analytics strategy. The first is the planning component which involves figuring out what data you need to capture and how often.
The second is implementation which includes capturing the data, storing it in an efficient way, and then analyzing it for insights.
Before you can plan out the implementation, it’s important to have a solid understanding of why you’re implementing an analytics strategy in the first place.
In many cases, there is a business need for better data that drives the implementation. It’s also important that when planning your implementation, you understand how often you’ll need to capture and store this data if applicable. This will affect how much infrastructure your team will require in order to support your efforts along with what types of software applications might be needed in order to capture the data in real-time or near real-time.
If done well, all of these components work together in order to create a comprehensive solution which provides insights into your desired areas of focus whether they are user metrics, loyalty metrics, operational metrics, or something else.”
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Here are some actionable tips you can use to create your data analytics strategy.
The first step is to get clarity on the goal or problem you are solving.
“The most important thing to consider is the data you want to measure,” says Kamyar K.S of World Consulting Group. “What is the business goal? How will you measure success? By having a clear goal, you can focus on getting the right data.”
Sasha Matviienko of Citadel says, “Begin with the end in mind, really. Understanding what exact problem you are trying to solve will help you ask the right questions when making decisions about your business. This will also create additional requirements.
For example, if some of the products/services you offer are more important to you than others, chances are, you would want to look at these products separately. If so, you may want to pre-build functionality that would allow you to do so.”
Natasha Rei of Explainerd adds, “Coming up with what data analytics are best for your company is knowing what particular questions you’re trying to answer. Once that’s clear, then it’s easy to figure out which are the best analytics models to use for your organization, revenue, marketing efforts, and many other areas of focus. This holds true across companies because good data collection makes any type of research easier. It’ll be helpful even when you’ve got mediocre resources even though there are plenty of times where resources are less important than identifying precisely what question needs answering.”
When you know the problem you are trying to solve, this helps you set a data analytics strategy that is aligned with your overarching business strategy.
Sharafudhin Mangalad of Edoxi Training Institute shares, “Try to identify your stakeholders, which is a group that influences your business. Stakeholders include management, shareholders, employees, customers, suppliers, government entities, and the community at large. Your strategic plan should consider each group relevant to your operations, how each group may affect your business, and what opportunities your business may have to engage, influence, or serve these groups.”
“Keeping client goals and business objectives in mind is the most important thing to consider when creating analytics strategies,” says Jordan Brannon of Coalition Technologies. “Whenever we create reports, we always tie metrics back to the clients’ goals, whether it be lead acquisition, brand awareness, or increased conversions. Moreover, we make it a point to focus on the target market’s demographics and interests, as doing so helps us create a relevant content strategy and a customized site experience.”
Tim Absalikov of Lasting Trend says, “The ultimate goal or result of using analytics is reports and decisions based on them. We always focus on the final goal. Strategy development is not a forecasting exercise, but rather an attempt to create an adaptive organization in which existing procedures force you to listen to your market constantly, rather than be content with marketing research once done. Periodically review existing methods of work, critically assessing them and looking for ways to improve, take actions that may not be visible in the monthly income statement, but are nevertheless important to the company. We define the monitored events and tools that we need. This approach allows us to implement only what we need, and nothing more. Instead of immediately starting to track 25 different events, we pay attention to the 5 parameters that we need right now. If data on certain events is not required to create reports and metrics that are of interest to us now, we exclude them.”
Matt Weber of Weber & Co. adds, “Having a strong analytics strategy can be the difference between success and failure for your company. A good analytics strategy should be based on the needs of the business, not what is trendy or looks cool on paper.
You should ask yourself: – What data do I need? – Where is the data coming from? – How do I want to use this data? – What’s my timeline? – What is my budget? – Who will be using the data and how will it be used? You should try to keep these questions in mind as you work towards developing analytics strategies for your company.
You may find that the data you need doesn’t exist, either throughout your company or on a specific platform.
In this case, you’ll want to think about whether there are tools out there that can make collecting the data possible – depending on what’s important to you, it may even be worth exploring outsourcing or paying a third-party company.
Alternatively, if it’s unclear where the data is coming from because of a variety of different variables involved in producing your product, you’ll want to establish an order in which those variables matter and prioritize them. Depending on your timeline and budget, there’s a lot of room for flexibility here – but the key is to make sure you’re considering all of your options and thinking about what can be done within those parameters.”
Knowing who will be reading and using this data to make decisions will help you design the data in a way that is most accessible for them.
“The number one thing to consider when planning an analytics strategy is determining who the analytics are for and what you want out of it,” says Nick Mattar of Digital Detroit. “Most teams look for analytics that they can use to make data-driven decisions, but there is also the need for sharing analytics with executives to show progress and ROI for the company. Making this determination will help you then plan out your key performance indicators and the context to provide around those data points.”
Doing market research upfront can help you better understand your audience and what’s possible.
“It is a good idea to do proper market research and come up with a few assumptions about your brand,” says Eden Cheng of PeopleFinderFree. “These assumptions should not be very positive but motivate you to grow. For example, ‘In the Financial year, Business XYZ can grow by 20 percent in comparison to previous FY’s 10 percent’. Thereafter, list all components that could help in ‘discarding’ this assumption by making it true. Also, list down the aspects that could confirm your achievement. So, it is kind of an internal goal campaign. And you will have to analyze the data throughout to make sure that you achieve it. A part of your strategy now explains how to do so. You may have 3-5 such assumptions, and analytics setups for each of them.”
In fact, once you know the desired goal and key stakeholders, it can help to map out the strategy in reverse.
“Map out your measurement strategy backwards from your KPIs,” says Alex Birkett of Omniscient Digital. “Tracking everything is a fool’s errand, especially if you don’t have the data science resources to use most of the data. You’ll get a lot of noise and little signal. Instead, think, “what are the meaningful events and goals I can track that will allow me to make better decisions?” These should all track back towards your actual business KPIs.”
Andrew Prince of Law Offices of James Scott Farrin agrees, “When planning a good analytics strategy, the most important thing to consider is the end goal. Start at the end and work backward to the beginning. This is key because spending too much time at the beginning can lead to tracking too much data that doesn’t impact the bottom line. Here’s an example: in SEO, the end goal is typically new clients. Start with figuring out how to track when someone becomes a client, then step back and track how they became a lead, then figure out how they got to the page to convert, then what keywords they searched to get to the website in the first place. By reverse-engineering the conversion process, it places an emphasis on the most to least important factors. A good analytics strategy places an emphasis on what leads to dollars and cents.”
By approaching this strategy in this way, you will ensure that the data you are collecting is actually actionable.
Moshiur Rahman of Miller Thomson LLP explains, “Effective analytics strategies should always focus on actions. Data is irrelevant if the organization doesn’t know how to give it meaning. Once an organization begins giving its data contextual meaning, it can cultivate strategies grounded in actions. From a digital marketing standpoint, analytics should enable organizations to take practical actions based on data. For example, this could be optimizing a landing page layout, simplifying the user experience of a lead generation form, or even something as simple as changing the colour of a call-to-action button to increase the click-through rate. So, analytics is only as good as its ability to provide actionable insights. If an organization’s analytics strategy doesn’t provide actionable data, it’s not a good strategy, and the organization should revise its strategy.”
Data is a strategic asset. You want to make sure that you are collecting and storing the most impactful data in the right places.
“Data is an asset,” says Mitch Chailland of Canal HR. “Really understanding this and wringing every bit of value out of your data that you can is foundational to every analytics strategy. Gathering as much data as possible and hiring smart people who can draw conclusions from this data gives you the vision you need to utilize your time and effort well.”
Patrick Crane of Love Sew adds, “A comprehensive and efficient data acquisition plan is the most important component of a good analytics strategy. Data acquisition should be given top priority when developing an analytics strategy because without valuable data coming through the pipeline constantly, the entire analytics process will be flawed. When developing a data acquisition plan as part of your analytics strategy, consider the volume, value, the velocity, and the variety of the data you need to flow into your analytics processes.”
A key piece of your analytics strategy is having the right data infrastructure.
“An often overlooked piece of a good analytics strategy is the technology infrastructure requirements to carry out the strategy,” says Brian Donovan of TimeShatter. “You can have an excellent plan, but you need the infrastructure in place to carry it out.”
Martin Luenendonk of founderjar adds, “The most important thing to consider when planning a good analytics strategy is to research the analytics that you need for your implementation. You should know what data you can get from these analytics and how you will use them to improve what you are implementing. There are a lot of analytics strategies that you can use but it is important to know the use of these strategies and what you can get from them.”
Editor’s note: Marketing reporting software like Databox will help you have effective oversight of your most important marketing and business metrics.
As you are building your analytics strategy, there will inevitably be data points you can’t collect right now or things you want to implement that don’t make sense at this point. Instead of letting this derail your progress, it is best to implement based on your current situation and then create a roadmap for future iterations.
Adam Nathan of CoEnterprise explains, “The foundational question to build a successful analytics strategy is to establish the data strategy for the organization at the current moment. This strategy can and will change over time but deliberately addressing it is the key to efficient use of resources, organizational alignment, and the bottom line.
There are three critical questions that are the bedrock of an analytics strategy. They are:
There are 8 possible determinations for these 3 items and their “flavor” shapes a specific approach to what data is captured, what initiatives are prioritized, and what resources are needed.
Again, this perspective will change over time, but this is a “don’t pass go” understanding for a strategy and an important level set. There is a modest effort establishing the needs of an organization but formalizing an analytics strategic posture will shape virtually every downstream requirement. This framework came out of MIT originally, but it has formed a fundamental perspective on how I work with clients to build their analytics strategy, determine projects, staff, enroll executive support, and more.”
Just because you have data doesn’t mean you need to use it. In fact, some people overcorrect and hoard all the data. This leads to analysis paralysis.
“Technology makes it easy to get overwhelmed with data and information today,” says Paige Arnof-Fenn of Mavens & Moguls. “The key is to not collect and measure everything but to focus on the 3 top drivers only and leverage the technology to gain important insights. A good analytics strategy does not let you get distracted once you prioritize what drives growth.”
Jonathan Aufray of Growth Hackers adds, “Data is key to business. However, you don’t want to be drowned with analytics and have metrics all over the place. Gathering data is easy but what you want is to analyze this data and understand it. To do that, what I suggest is to focus on data points that are important to your business. Metrics that help your business grow and help you move the needle. Don’t focus too much on vanity metrics such as likes or followers but rather focus on number of qualified leads you generate, conversion rate, lead to sales ration, revenue, RoaS, ROI, etc.”
Another easy trap that you can fall into is sticking with the simplest data that you can find instead of the data you actually need. This often leads to tracking vanity metrics that don’t correlate with the core business strategy.
“The most important thing when planning an analytics strategy is to sort out your key performance metrics away from vanity metrics (numbers that look good but mean nothing to the bottom line),” explains Vaish Pathak of Mobile Experts. “Attribution tracking for ROI should be in place to effectively measure different steps of the marketing funnel, as you can only improve through measurement. If you only look at vanity metrics, your report can look great, but it can lead you to a dangerous position where you end up burning resources and ad spend on campaigns that don’t generate a direct return.”
Once you have your strategy in place, it is time to build a project plan or roadmap for you are going to implement it. This requires operational planning.
“You have to do operational planning,” explains Toby Dash of Five Star Skincare. “It would help if you plan when everything is going to be done, then actually do it. There is no point in brainstorming an analytical strategy for two days, then not following through.
Without operational planning, daily priorities take over, focus shifts, and everything discussed and decided on during the two days of brainstorming get forgotten. There is no point in planning strategies if you do not plan when and how they will be implemented.”
Because analytics ties to every part of the business, it is helpful to think of this operational planning as part of a larger business ecosystem.
Shahar Erez of Stoke Talent says, “It is wise to use a business analytics ecosystem approach in deciding which metrics to track. Be sure what big picture you want and then start choosing the right information sources and methodology. Always align whatever business analytics capability you put in place with your overall strategy if you want to make a difference. Otherwise, you are throwing money away and wasting highly skilled people’s time.
Use the findings from business analytics in a systemic way directly linked to what other business units or departments are busy with. Think of the business as a coevolving ecosystem with business analytics as one agent in localized and system-wide changes. Analytics and the rest of the business should move together in tandem and in a coordinated way.
Both need to listen to each other carefully. Make sure there is coherence between what is observed by analytics and what is done by everyone else.”
Ultimately, your success will depend on how accessible the data is to the key people who need it and how often they use it to make data-driven decisions.
“The success of an analytics strategy depends on how well it’s integrated into work processes,” says Brad Tousenard of SpinupWP. “Analytical models can generate rich insights, but their value is only realized if managers and employees can understand and act upon them. Output that’s too complex can generate confusion or mistrust. The solution often lies in utilizing appropriate tools that integrate analytics into day-to-day processes to deliver quantifiable business benefits.”
For example, Mykola Tymkiv of MacKeeper says, “We are a big company with over 800 employees, which is why our top priority is to ensure that our analytics strategy is coherent across the teams. For example, our customer support and sales teams align their analytics to make better decisions. Both teams use Salesforce to optimize their processes, so it’s important that our analytics strategy includes data from every department, and is used to improve every element of our business together.”
You already know data is essential for efficient decision making. But collecting, processing, organizing, presenting, and analyzing data can be so challenging! Seeing all those numbers gives you an instant headache and you feel like you spend ages compiling the report that your clients or managers end up only skimming over and asking you a million questions – your effort was for nothing.
The whole point of writing a data report is to lay the groundwork for effective data analysis and drawing the right conclusions so you can make further decisions for your business. And can it be less painful?
Yes, it can!
We created Databox with the goal of making data reporting less time-consuming, tedious, and demanding. Instead of wasting your time on manual activities, you can pull all your data in one place in seconds, create custom metrics with ease and adjust reporting dashboards to your needs.
What’s more, you can automate custom calculations, which makes calculating metrics from several sources or tools 10x easier than before, without any coding or spreadsheets. Now you won’t need to spend hours calculating ROI, conversion rates, and other relevant financial metrics stakeholders typically want to know about.
If you think we stopped there, think again. Databox also enables you to build custom metrics with Query Builder’s dimensions and filters so you can have a more detailed insight into your performance. You can also connect your account with any of the 100+ integrations we offer: Google Sheets, Google Ads, Google Analytics, HubSpot, Zapier, Stripe, and pull data from anywhere – even a SQL database or custom API, in a safe, fast, and simple way.
Can you believe it’s all found in one place, a single dashboard? Actually, you don’t need to believe a word we say – test your own dashboard and see for yourself: sign up for a free trial today.
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Jessica Malnik is a content strategist and copywriter for SaaS and productized service businesses. Her writing has appeared on The Next Web, Social Media Examiner, SEMRush, CMX, Help Scout, Convince & Convert, and many other sites.
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