There are about 200 reports in my inbox right now. A couple more stories I can access in a dashboard but I won’t. All these reports are essential. Lead-to-sale ratio. Average order value. Sales cohort. All important. These are data — useful blocks that, stacked on top of one another, — lead to…wait, what is their purpose again?
Increase sales, I suppose. Increase data, increase sales. Or is it? Maybe all the time — or not at all if NOT utilized right.
Making profitable sense out of data and analytics isn’t easy, but at least it doesn’t require you to wear a suit or be threatened with kryptonite. You will, however, have deep accountability knowing you have data that could tremendously give you a competitive advantage. Now, I'm confused about which is worse: get bitten by spiders and obtain superpowers, or get drowned in the data sandpit!
According to Statista’s study on online shopping, eCommerce 1.8 billion consumers are spoiled with choices. Also, in the eCommerce market,there are roughly 12 to 24 million stores worldwide, clubbed together with Magento, WooCommerce, and Shopify — all competing being at an all-time high. More than ever, it is becoming increasingly crucial for businesses to diverge and innovate, make use of their data intelligently, to grab the consumer’s attention and increase sales.
Now, I wanted you to think of the most recent item you bought online.
Clothes? Items from the health and beauty category? Electronics? Home and decor?
If I remember it right, the last time I bought online was a car phone holder. From then on, every time I visit the e-commerce app where I bought it from, all I see are items related to cars! Almost everything! Dashcam, all kinds of cords as if each is unique and functions differently. A few days ago, I bought 2 cords! (Can’t help but laugh while confessing this!)
But that’s just me. How about the other 1.66 billion online consumers purchasing items online and expecting a personalized user experience? Imagine hundreds of thousands or even millions of people crowding your ecommerce site or app, coming from different marketing cylinders.
You as a business or as the one handling the ecommerce site — being faced with all these diverse audiences, with different preferences, coming in from different channels — can potentially get lost in the myriad of data you're getting.
Familiar scenario? I bet it is.
Last September 26, Propelrr was invited to take part in Seamless 2018, one of the largest marketing conferences in Asia, to talk about Unleashing the Potential of Your Data at the E-commerce Marketing Theater. The booth was filled with digital marketers from various companies and industries.
Going back to the question “How do you handle a crazy amount of data and use it to your leverage?” Where on flat-or-round-whatever earth would you start? Put all these things in a framework — a deliberate and malleable thought process — so you can easily map out where to start and how to execute your strategy.
Put all these things in a framework — a deliberate and malleable thought process — so you can easily map out where to start and how to execute your strategy.
That’s another horrifying “how” and we understand that digital marketing is a different mammoth on its own, thus, we devised a digital framework so you can easily map out your plan.
On this article, I want to take a moment to zoom in to the analytics component.
As a business, you are tracking massive data that can be categorized as Web Analytics, CRM Analytics, Pay Per Click or Paid Channel Analytics, Mobile App, and Business Analytics, among others.
If last year's highlight about the e-commerce industry was all about building solid infrastructure, the next thing all businesses should be addressing is about unleashing the potential of their data to give you business intelligence and provide a seamless experience to your customers.
After all, businesses in the e-commerce sector are not selling products. They are selling convenience and personalized experience — something they could hardly get when visiting a physical store. So where does data come to play?
Shoprr, The Hypothetical Client
Take this hypothetical client, Shoprr, as an example.
Shoprr’s overall objective is business growth. Well, I don’t know any businesses who don’t aim for growth.
They cited ROI as one of the indicators of growth. They then identify, the metrics that affect their ROI and arrived at the two metrics: Customer Acquisition Cost (CAC) and the Return on Ads Spend (RoAS). Shoprr used these two metrics to drive marketing decisions for budget setting, channel optimization, and even resource allocation.
Later on, as data continuously pile up, Shoprr realized that having low CAC and high RoAS do not reflect real growth, but is neither a sustainable one. They may acquire customers at a lower cost and even register very high RoAS for their campaigns, yet the overall profitability wasn’t stable and was heavily dependent on discount-centric promotions and campaigns.
The Customer Journey
To further illustrate, let’s picture the customer journey of this hypothetical client.
Say Maria, about 28 years old, browses through the Shoprr App and buys 2 to 3 maternity dresses
“Will this dress fit me well?”
“Is this stretchable?”
“What color would best suit me?”
All those considerations.
You, as the brand, would be able to track all of these data. What channels did she come from? What specific campaign led her to the app/store? How many items did she purchase? The order value, pages visited, and more.
After the sale, you can also track different metrics and data like delivery, customer rating, return visit, and repurchase data.
If you are this hypothetical client, Shoprr, how would you process all these data to ensure you work your way to grow your business? Are CAC and RoAS enough to predict your business growth?
Moreover, how would you use these data to make sure Maria would be a valuable customer for your business in the long run? Given all the data available, how would you make shopping feel more human and personal for your consumers?
The Potential of Data
Quite soon, Shoprr started considering the performance and the secondary metrics, predictive indicators, and other non-numeric factors. Through regression, Shoprr was able to identify that it isn’t just CAC and RoAS that determine their estimated ROI but also the Customer Lifetime Value (CLV).
Customer Lifetime Value is the potential profit attributed to the entire future relationship with a customer. As no analytics automatically churns it out, most businesses would need to build their own CLV model commonly using Regression Analysis.
Why does CLV matter?
CLV helps businesses make intelligent decisions concerning marketing, product development, customer service, and potential sales.
- Marketing: What does my customer acquisition cost look like?
- Product: Which products would best fit my customers?
- Customer Service: What is the cost to engage and retain customers?
- Sales: Who are the customers that need more focus for sales targeting and upselling?
Going back to the customer journey, Shoprr made use of all the captured data to identify Maria’s CLV potential which led to better opportunities in maximizing the ROI potential from her. They also use the data to plan what ads would better resonate with her based on assumptions made from her previous transactions. They know when to retarget her, where and what channels, what promotions/discounts would make her put the items in the bag and check out.
The End Result: Growth!
Imagine scaling this to all your customers online. You can segment them into granular groups and determine the best-fitting offers and products, at the same time make the shopping experience for your customer better and less infuriating.
What was the result of all these?
With the power to predict and assign a CLV value to each visitor — new and returning — you will be able to come up with highly relevant and targeted campaigns for its customers, allocate budget and resources to acquisition channels, and launch campaigns that have a significant impact in generating better ROI.
Moreover, identifying your CLV would help you save on marketing costs as you focus on high-value customers and improve on other areas such as logistics, which has a greater impact on customer experience.
After the dizzying math, how exactly would you start unleashing the potential of your data?
Steps to Unleashing the Potential of Your Data
Although many marketers cringe at the idea of the mathematics behind it, data will give you stronger and long-term ROI. It only takes six steps to unleash the potential of your data and lead to growing your business online.
Here are the steps, guide questions, tools, and templates you can use:
1. Map Out Your Objectives
What are you trying to achieve?
Guide Questions:What is the primary goal for your business?
What are the things that could impact your goal?
If you set out into digital without a clear definition of your goals and what you hope to achieve, it could be almost as detrimental as getting left behind the digital transformation. Ultimately, not mapping out your objectives will lead to frustration, not to mention the total waste of time and money. Follow the guide below and map out the direction you want to work towards.
2. Identify Your Data Models
What questions are you answering?
What is the main factor that affects my primary goal metric?
Can I predict my KPI performance? How?
What is the sales journey of my customers?
How to identify your data models:
- Define the types of information that are needed to be held in the database.
- Identify the relationships between the different data components.
- Pin down anticipated use cases related to the different datasets.
By now, you should have a concept of what you need to create and the types of interactions that are necessary with the data and the database.
3. Choose Your Analytics Platform
Which tool should you use?
Does it have the features you need?
Is it easy to learn, use and navigate for all stakeholders?
What are the costs and the promised value?
Like any other technology investments, the analytics platform should help speed up the process, help you crunch massive amount of data, and directly support your current business requirements as well as future company goals. How exactly would you choose your analytics platform?
- Interview your internal stakeholders and outline what role they will play in the selection of the platform.
- Prepare your data governance plan.
- Identify high priority and low priority features and functions.
- List down at least 3-5 platforms to choose from.
- Come up with a qualification checklist. You can use the criteria below:
- Analytic Approach
- Features and Tracking Types
- Connectivity and Integration
- Professional Services and Support
- Data Storage Options
- Legal Compliance
- Supplier and Software Reliability
- Ongoing and Future Costs
- Data Ownership
There are a lot of choices for the analytics platform. I know. It's both a blessing and a curse. Narrow it down using the checklist above.
4. Identify Your Analytic Models
How do you set up and implement?
What report segments do you need to answer your questions?
How should you present your reports? Dashboard, tables, graphs?
Will you need to use cross-tabulations or single dimension reports?
To put it on record, an analytical model is a mathematical equation that describes relationships among different variables in a data set.
Analytical models are key to understanding your data, generating data-informed predictions, and making data-driven business decisions. Without analytic models, it's nearly impossible to gain insights from data. Can you imagine yourself staring blankly at your data and then viola! Insights! Nah. Let's not picture that out.
How to map out your analytic models:
- Review the objectives.
- Identify hypotheses and prioritize the data sets based on the objectives identified.
- Sift through various data sources to find all the data sets needed. Document each potential data set using the checklist below:
- Access Method
- Data Characteristics
- Business Rules
- Data Pollution
- Data Completeness
- Data Consistency
- Standardize and enrich the data to meet requirements for data processing techniques. Eliminate variables and values that aren't relevant.
- Deploy the model.
- Track versions, audit usage, and manage a model through its lifecycle
Analytical models are powerful—make predictions to streamline business processes, reduce costs, increase revenue, and improve customer lifecycle. Got dazed yet? You can consult your data modeler or analytics agency for better understanding.
5. Insights, Analysis, and Validation
What does your data tell you?
What stories do you get from your data?
Were your data able to answer your questions?
Are you able to measure your performance?
The most exciting part! There simply isn’t any room for false assumptions when you have every data to tell what to and what not to do.
How can you improve what you're doing?
Quite the contrary, data can outgrow the business, the analytics platform, and the models you built if you don't continuously strive to improve.
To achieve real improvement, the impact of change must be measured, and the change must be continuous. This makes it possible to determine if you're going to either survive or dominate or maybe— fail.
We’re on the precipice where data could determine and destroy the future. Companies increasingly find that their economic value is a function of the strength of the data they analyze. But as some companies are just waking up to this new reality, others are already crushing it.
Is it too late? I’d say not yet.