How E-commerce Data Analytics Boosts Marketing Operations and Performance
Having the capability to harness data effectively can inform who the right audience is, how and when to reach them, what the optimal price point is and compare approaches if something isn’t working.
With global e-commerce continuing to boom and competition for customers fierce, Fran Quilty, co-founder and CEO of Conjura, discusses why marketers must find innovative data analytics strategies to extract, interpret and depend on data to inform decisions affecting customer behavior, the customer journey and subsequent impact on sales.
The global e-commerce market is expected to exceed $5 trillion this year – and increase to over $7 trillion by 2025. Amazon recently reported more than 300M items sold worldwide on Prime Day, with the US consumer spend up 8.5% from last year, reaching a whopping $11.9B.
With over 25% of the planet’s population shopping online and an additional 900 million digital buyers in 2021 than 2020, the significance of e-commerce to the global economy is clear.
However, this astronomical growth has been accompanied by drastically rising consumer expectations. This reality is demonstrated by 88% of products added to online baskets not converting to actual purchases. A poignant reminder there is still a battle ahead to close the knowledge gap between a business and its customer base, and marketing teams are in a prime position to lead the charge. To do so, brands must excel at extracting, interpreting and utilizing data to make intelligent decisions and boost operations.
See More: Headless Commerce: An Enabler of Differentiated Customer Experience
Behind the Data
Undressing data reveals customers’ intentions, which parts of a website they most frequent, which channels they interact with, and which products are most appealing – and from an operational side, stock levels, inventory management, forecasting, and pricing, among others. In short, harnessing data effectively can inform who the right audience is, how and when to reach them, what the optimal price point is and compare approaches if something isn’t working.
McKinsey reports that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain them and 19 times as likely to be profitable. For marketers who are constantly fine-tuning techniques to understand and address customer demands and expectations, well-sourced and cleansed data goes hand in glove with hyper-targeted and easily analyzed marketing campaigns.
At What Stage to Use Data Analytics
Unlocking relevant, useful data to undergird systematic marketing approaches should be the goal of every business, from the bedroom start-up to a FTSE 100 company.
To begin with, there are two questions businesses must ask themselves:
- Am I a high repeat order business (such as a beauty business)? – these will generally optimize marketing spend on customer lifetime value against the cost of acquiring customers, i.e., they have an acceptable payback period on marketing spend)
- Am I a low repeat order business (such as home appliances) that focuses on making an instant profit on every dollar of marketing spend?
Data analytics help companies make this determination and then align the marketing and business decisions, which flow naturally as a result. For high repeat order businesses, this may mean focusing on benchmarks such as conversion rate or customer retention rate, orders per customer, the time between orders or basket abandonment rate. For low repeat order businesses, data points may include cost per acquisition, average order value, average sale price per item, and revenue and cost per visitor.
Ideally, companies should implement data analytics from day one. Unsurprisingly at such an early stage, there won’t be a well of data to draw from. Even in relative infancy some external analytics solutions will help companies validate the right channels for the right audience within the right budget by providing industry benchmarks from companies with a similar DNA. The data analytics solution will be continually engineered as the company develops.
Creating More Accurate Budget Allocation
Many data collections work in tandem with marketing solutions, ensuring decisions are made in real-time with the most up-to-date information. For example, budget allocation – in marketing terms, this may be for paid or organic traffic, branding or conversion rate optimization. Data analytics enables smarter budget considerations and acts as the guardrail for the implementation of marketing plans.
Taking this a step further, data analytics can examine each marketing channel in each territory and determine whether the respective key performance indicators (KPIs) are sufficiently strong, which will lead to budgets being drawn up for the coming week. Data analytics is the smartwatch for marketing teams, alerting them to discrepancies and inducing changes in KPIs to identify the underlying cause(s). Fundamentally, data helps reduce the budget for poorly performing campaigns or increase the budget for top performing campaigns.
Automation of Data Analytics
Automating data analytics results in fewer human errors that come with manual analyses and increases marketers’ confidence in their decisions and processes. If a KPI changes, for example, rather than the marketing team manually altering a setting in their marketing platform user interfaces, an analytics solution can be plugged in, which is activated automatically. The real-time analysis allows marketers to make informed decisions that can prevent bottlenecks. To notice changes in KPIs may take months without an automated data solution.
An impactful data analytics solution will provide one common attribution methodology across all marketing channels, allowing marketers to obtain a single source of truth. They will be able to operate with a bird’s-eye view of relevant data, which all teams can access, reducing misalignment and inconsistency. It also leads to more reliable audience segmentation, encouraging more personalized approaches from a company to a customer.
Invariably, a customer journey has plenty of steps, hurdles and potential pitfalls. When platforms work in harmony, ensuring cross-functional teams can access the same information, activity reports can be produced to measure success, or automations can be set in motion based on insights, and customers are likely to be better engaged with and retained.
Without data analytics solutions, it would be difficult to understand how each marketing team and channel work together to meet company goals, and it becomes complicated to assess the value that each channel brings in relation to another and to track the rate of growth of each channel clearly and concisely. The customer journey is illuminated by mining data from multiple sources and storing them in one place, and better insights are developed.
See More: The Power of Cloud Hosting for Successful Ecommerce Marketing
Data Analytics and Social Media Influencers
Consider influencer marketing, where brands choose various influencers to sell and promote their products on social platforms like TikTok, Instagram, etc. They often provide the influencers with discount codes that they then share with their followers via their stories or daily posts. To assess the effectiveness of individual influencers, good analytics solutions collect all of the Influencers’ sales and traffic data on one platform instead of analyzing them on separate channels via each influencer’s unique discount code/URL link. This creates a single source of truth, making it much easier and more effective for companies to reward high-performing influencers with better terms and remove the less effective influencers who don’t have the right target audience.
With the online retail ecosystem saturated with companies vying for a market share, every advantage a business can leverage is priceless. Optimizing and refining marketing activities is a crucial way to achieve this. Intelligent methods of marshaling and analyzing data, enabling automation to support real-time marketing decision-making and KPI adjustment, and subsuming all data points into one single source of truth for ease of access, improved multi-channel synchronization, and data-bound results can all help businesses keep one step ahead. Incorporating a dynamic data analytics solution into a business marketing function will ultimately ensure they are customer-centric, channel-wise and sales-optimal.
What data analytics tools would you recommend for ecommerce businesses? Share with us on Facebook, Twitter, and LinkedIn.
MORE ON DATA ANALYTICS
