Many clients come to us faced with an influx of data that seems impossible to sort through. Often, this originates from incoming data that has grown well beyond what was initially anticipated and ultimately leads to a new diagnosis in the digital world – analysis paralysis.
In most cases of analysis paralysis, too much emphasis is placed on increasing the diversity of marketing strategies and tactics to stay on trend, but not enough attention is paid to prove ROI. In 2018, Rightscale reported that companies estimated roughly 30%-35% of their spend on digital tools was wasted.
What brands are faced with is usually an enormous amount of structured and unstructured data traversing site analytics, pay-per-click performance, social engagement, digital conversation, in-store sales, e-commerce, etc.
How did the data get so big?
Big data has become more than just a buzzword. In 2019, tech marketplace G2 found over 2,000,000,000,000,000,000 bytes of data was created every single day.
This astounding number can be tied to the growth of the Internet of Things, or IoT. People are constantly engaged with devices connected to the internet – contributing to that two quintillion bytes of data mentioned. In fact, by 2021, Forbes predicted the average American is expected to own 13 internet-connected devices.
Maybe you’re one of the lucky ones – your KPIs and goals are easily measurable, and your business isn’t rushing into a 2020 big data planning frenzy. But beware – this data revolution shows no signs of letting up. To help ensure you’re putting your best foot forward this year to control the chaos, make every dollar count and avoid paralysis when it comes time to analyze the data, here are a few of Leap Group’s tips.
Our first set of tips center around the understanding the Three V’s of your data:
We’ve been analyzing data at Leap Group for over two decades and learned pretty quickly the amount of data you have and where it’s coming from only matters when the integrity of the data can be trusted. Start thinking now about what’s important to you and your process of analysis when it comes to what is considered reliable and what is not.
Take a thorough inventory of the tools and platforms used across all verticals to gauge how many pit stops you have in your data analysis journey. This will help improve the efficacy of future data collection, storage and manipulation.
We’re used to working with structured data like names, credit card info, etc. that fit neatly within rows and columns of an Excel spreadsheet. But today, we are increasingly faced with unstructured data like surveillance footage, social media images or snippets of digital conversation that cannot be processed using conventional methods. Identifying where your data falls among these two categories sets the stage for deciding on the appropriate method of categorization and analysis. (Unsure of what kind of data you’re working with? We can help!)
Our second set of tips identifies ways to improve existing data:
4. Evaluate Your Tech Stack
Start with the basics to ensure you and your team have clearly defined, efficient processes to eliminate redundancies in your workflow. Are tools and platforms helping or hindering data analysis? Look for more “all-in-one” solutions, such as dashboards.
5. Monitor Data in Real Time
The sooner an anomaly is discovered, the easier it is to solve – especially when it comes to situations like a sudden drop in traffic to a site, or a sudden increase in traffic from a particular source.
At Leap Group, we set alerts that inform our team if there are any sudden spikes in incoming data across any of our platforms. This allows us to react to the right issue on the right channel at the right time.
6. Segment + Filter
Segments allow you to isolate and analyze a subset of data. For example, when analyzing all of the users who visiting a particular website, how many of them originated from a specific region, purchased a certain product or signed up for the newsletter. We use filters, on the other hand, to limit data – excluding site traffic from an IP address, or only including traffic from a particular hostname.
7. Visualize Your Data
I’m not sure about your brand, but most of our clients do not respond well to large Excel outputs. Remember that “all-in-one” dashboard I told you to use internally? Turns out, clients love them too!
Dashboards are an efficient way to synthesize data from infinite sources into one easily accessible platform. For (amp) – our media and amplification agency – I wrote about how we use dashboard to take reporting to the next level!
The final set of tips is geared toward next steps following initial analysis:
8. Collect More Data
I already said it – there is often too much data. Once you have performed the first round of analysis on your data, take some time to see if you are missing anything that could improve your view of existing data. Helpful clues often live outside your data set (customer reviews, digital conversation) so don’t look at your numbers in a vacuum.
9. Run Experiments Regularly
A/B testing creates actionable data that often can yield results using low data thresholds. Experiments help us avoid unnecessary risks by testing hypotheses on a smaller scale before deploying significant strategy changes.
10. Write the Narrative
My final piece of advice: Data-driven storytelling is much more impactful than a list of numbers. We found that analytics alone have little merit until paired with insights. Make sure any report you pass on to your client can be easily consumed with or without an accompanying presentation.
If you fall in the 95% of businesses with a need to manage unstructured data, according to Forbes, an agency partner like Leap Group can help! We have a dedicated team of digital performance professionals that work with clients across different verticals in all sizes. This team’s only responsibility is to turn data – big or small – into a story and look past the vanity metrics to deliver scalable, robust insights and recommendations that won’t cause analysis paralysis, but instead will help them lead successful campaigns.