How can you collect e-commerce data more efficiently?

Here's a step-by-step guide: 1. Set Clear Objectives: Before diving into data collection, establish what you're hoping to achieve with your e-commerce venture. Are you aiming to increase sales, enhance customer retention, or maybe improve product visibility? 2. Identify Key Performance Indicators (KPIs): Once objectives are set, pinpoint the specific metrics that will track your progress towards these goals. For instance, if your objective is to boost sales, a relevant KPI might be the 'conversion rate.' 3. Prioritize Metrics: Not all metrics are created equal. Determine which ones will have the most significant impact on your objectives and prioritize collecting data for those.

Your KPIs must tie back to your business goals. If for example the priority is profitability, you will need to closely monitor metrics like first purchase profitability, Incremental RoAS, gross margins, net margins... etc., If your objective is cash flows, then monitoring metrics like inventory on hand, AR/AP, cash balance... etc., will be most important. Start with the business objective first. The right metrics will be clear afterwards.

Agree , It’s critical to take a “measure what matters” approach and avoid the trap of metric paralysis . The key is to identify a set of select measures that are “actionable” and can help you drive business impact i.e.revenue , margins . It may be prudent to take a less but better approach and ensure that we have right set of “action metrics” instead of a huge scorecard .. make action your priority not just measurement !

That is true, once your SMART goals are defined you should focus on both Sales and Marketing channel to maximise revenue and minimise cost.

I love the SMART acronym but I would a second “A” to stand for “Agreed-Upon”. You must agree with internal stakeholders (your team) or external partners (your agency) on what those KPIs are. If your teams are not aligned from the onset, everything you do to optimize will be largely fruitless. All parties must be not only aligned to the same goal, but also the way by which it’s achievement should be measured.

In our multi-data sourced project, integrating e-commerce inventory into brick-and-mortar locations presents a significant challenge. Effective data clustering is paramount; it enhances accuracy and transforms data into actionable insights. 1. Clustering Data and Multi-Sourced Data Clustering: Grouping similar data points, especially from multiple sources, streamlines insights. For example, we can group customer data by demographics. 2. Related Flows: Understanding data movement within the organization is vital. It illuminates how data interconnects and influences processes. 3. Customer Footprint: Analyzing customer interactions provides valuable insights for decision-making.

Efficient e-commerce data collection hinges on knowing what data you need and why. Pinpoint key metrics aligned with your business goals, whether it's customer behavior, sales trends, or marketing ROI. APIs are crucial for easy data extraction from e-commerce platforms. While tools like Google Looker Studio or Google Analytics offer insights, they can be restrictive. Embrace tools like Tableau for greater flexibility in manipulating and visualizing data. Tableau's visual narratives unveil hidden trends and insights, essential for informed decision-making. Efficient data collection isn't just gathering numbers, it's transforming them into actionable intelligence.

As part of your goals around data collection, also be clear about specific use cases of what your business would like to achieve from the data. Use cases can vary from reporting & insights to workflow automation for specific tasks. The more clear you are on the desired outcome, the more targeted this first step of collecting data can be.

Efficiency in e-commerce data collection isn't just about using the right tools, but also about streamlining the entire data workflow. From setting clear objectives to automating collection and conducting regular audits, an efficient approach allows you to focus more on strategic decision-making rather than getting bogged down by data management chores.

I always like like to start with building an e-commerce sales model with quarterly and annual targets that are specifically tied to business KPIs based around your business and market. For example, if your direct-to-consumer coffee business that sells subscriptions then your KPIs might be prioritized around improving customer churn and analyzing customer cohort data. While if your a consumer technology business like ChargePoint (CHPT) that sells EV charging hardware you might be solely focused around new customer acquisition and referrals over churn. Define your targets for your business and market, identify your KPIs to achieve those targets and build your data collection strategy around it.

For managing your Retail Ecommerce business, you need a unified platform which can tie together elements such as: Retail Media investments, inventory positions, sales and marketing KPIs, digital shelf analytics, share of voice and market share. You may need to extend this to cover multiple sales channels, including your DTC channel as well as reatailers like Amazon, Walmart... etc., if selling wholesale. This can be done by using a selection of specialized tools, or by buying a unified platform which covers all these metrics.

These days, the "e" in e-commerce stands for efficiency. In a world where digital platforms shape the very essence of consumer interactions, it’s not just about going online; it’s about doing so with precision and purpose. Embracing a multitude of channels is no longer a luxury but a necessity. Yet, with more channels comes the peril of overwhelming complexity. The antidote? Advanced AI integration. But don't be swayed by every glittering algorithm. Seek those tools that provide a panoramic understanding of the marketplace. As striving businesses delve deeper into the digital domain, the mantra should be: simplify, strategise, and stay ahead. Scalability is as much about optimizing processes as it is about expansion.

Analytics is very important! To gather information about your website and mobile app, use tools like Google Analytics. These tools help you see what people do on your site and in your app. You can find out which pages they visit, how long they stay, and what they buy. This information helps you make better choices about how to improve your website and app for your customers. It's like having a map that shows you where people go and what they like.

Choosing the right tools to achieve your business goals is essential, however perhaps more important is ensuring that these tools are compatible with one another, and that you have someone on your team who understands how they work. When consulting I often find setups where a website has a bunch of great software and programs implemented, but either the software function is restricted based on incompatibility, or the company isn’t taking advantage of what the software does due to limited staffing or lack of product knowledge. These products are excellent tools, but as with any tool, the value they bring to your business will be tied with the skill and bandwidth of the team who manages them.

When selecting a tool consider what level of data quality you need to have in place (especially when it comes to eCommerce platforms ensuring 99.9% data quality comes at a cost) - and also how you envision to interact with the tool, whether that's through their app or using APIs to get access to the data at scale for further processing.

Batch Your Data Collection: Instead of pulling data at different intervals, batch your data collection into regular, pre-determined time slots. This cuts down on manual intervention and ensures you're getting a holistic view of your e-commerce operations at a consistent frequency.

There are dozens and dozens of tools available. On major platforms you can have plug ins and all sorts of inexpensive actionable tools for almost any type of data (textual, visual, financial, etc.) But if your company is of any size, then stay away from the inexpensive tools. Why? Because if you take off-the-shelf, you have what everyone else has, you will not have any real customization and it will not grow with your company. Have an emergency at 5pm on a Friday? THAT is when you want to have a business partner who can help you trouble shoot and find solutions. That is why you pay extra. Your brand is unique. Don't settle for ordinary.

I'm a strong believer in keeping it simple (KIS). Having worked as a Shopify merchant and managing multiple brands, I tend to stick to Shopify analytics under $500K a year, and more then that I tend to look at bigger price point platforms like Polar analytics. I've used Excel and Shopify Plus analytics for brands all the way up to a multi-million dollar brand in e-commerce. However, if you have an OMNICHANNEL brand is might make sense to spend 6 figures a year on a premiere solution like Neilsen Data to track competitor sales data in your category. My recommendation is to figure out which KPIs that are relevant to your business and keep it simple.

Great points on tool selection. Additionally, investing in staff training to maximize the utility of these tools is essential. A tool is only as effective as the person wielding it. Regularly updating and upskilling your team ensures the tools are used to their full potential, extracting actionable insights from the data.

People often are trapped in collecting all that data themselves; in the worst case, they may even do it manually. Take competitor pricing as an example. Instead of repeated checking prices of your competitors by hand - which can only be limited to a specific group of products and competitors - you can simply automate the whole price monitoring workflow by implementing a specialized online price tracking tool and scale the size of your coverage as much as you like - tens or hundreds of thousands of products across 100s of competitors, for example.

When looking at automation, you need to ensure you are not only buying a solution that automates collection, but more importantly automates actions one takes off of that data that you're collecting.

Here is one thing I have found helpful is the use of CRM in collecting data which have proven to be efficient and good, example of such CRM is Gohighlevel (an all in one system)

Create Data Templates: Use customizable templates for different kinds of analysis you frequently perform. This way, whenever new data pours in, you don't have to start from scratch—simply plug the data into your template. Leverage Real-time Dashboards: Platforms like Google Data Studio allow you to create real-time dashboards. Instead of manually gathering and assessing data, these dashboards provide you with immediate insights as the data comes in.

I tend to use for example with an e-commerce brand I might use Shopify analytics and then a tool like POWER BI. However, you could simply use excel and hook it into Shopify at the most basic level. The key here is to make sure that you can see your basic e-commerce transactional data like CVR, AOV, return customer rate. While the higher-level data that you would capture in POWER BI would be more higher level, revenue, CAC and LTV data. That's generally how I like to break it down.

Looker Studio, previously known as Google Data Studio, is a handy tool for automating data collection and reporting. You can easily collect and showcase your business' main KPIs by creating a dashboard tailored to your brand's needs. I would also say that the true beauty of Looker Studio is the fact that it allows you to see right away if there is something off in any of your sources and channels such as Google Ads and Meta Ads Manager. This tool is highly recommended for tracking your sales funnels.

Integration is the future! Alongside, it's beneficial to have regular system health checks. As you integrate more tools, compatibility and system efficiency should be monitored to ensure that no tool impedes the performance of another. This proactive approach prevents potential system bottlenecks or conflicts.

This is really where all the otherwise-would-be-very-successful data-driven pieces of e-commerce tech fail. Merchants - who fancy automation and data - often get stuck at a point where they feel bombarded by some data that they start failing to digest and react upon - something like data fatigue. There comes the importance of how neatly those data are talking to each other and ideally making some of those straightforward data-driven decisions that people primarily make on Excel files with fundamental formulas. Therefore, any bit of automation should not stop at the data collection and delivery level but instead go ahead and make some of those business decisions as confidently as the merchants themselves.

Keep the dynamic nature around data in mind. Even with APIs of leading eCommerce platforms, versions change, data tables evolve (sometimes announced, many times unannounced). Ensuring clean and consistent data will be an ongoing task, not a one-time effort.

AI-Powered Insights: Use machine learning tools that can sift through large volumes of data to identify trends, anomalies, or opportunities that you might miss. This can not only improve the efficiency but also the accuracy of your insights.

As much as it is important to remove bots or spams, it is imperative to exclude data from internal IP. Lot many times our team is testing from a certain IP or domain which might show skewed result.

Indeed, 'Garbage in, garbage out.' While cleaning data is essential, establishing protocols to maintain data hygiene from the outset can minimize the need for extensive cleaning. Implementing standardized data entry guidelines and periodically revisiting them can save hours of cleanup downstream.

To effectively act and analyze e-commerce data: Set clear goals, and realistcs too, you can´t have it all at once, even for goals its necessary prioritization. Collect relevant data, don´t waste time and money in data that you will never use Use tools for analysis and visualization and teach the stakeholders the best way to access and visualize the data. Make data-driven decisions, if your stakeholders understand where the data came from and what it is saying will be easier to make data-driven decisions Continuously refine strategies based on insights Remember that e-commerce data analysis is an ongoing process. Regularly revisit and refine your strategies based on new data and changing market conditions to stay competitive and up to date

That's why reviewing your KPIs with your team on a weekly, monthly, and quarterly basis is super important. Collecting data is important, but using and analyzing it is more important.

AI and machine learning today will enable you to take action out of your hands entirely and allow the technology to do the work for you. Automate the administrative, rules based tasks based on data, and save your team's efforts to be focused on making decisions which require creativity and strategic thinking that machines simply can't do today.

One thing I've found helpful when analyzing data is to ask myself what's the story behind the numbers. I also invite you to put your critical thinking to the test with this question because it would give you insights into the information collected on your reports and what would be the best course of action to take based on your main business goal.

Acting on data requires can require more complex logic, something that AI's are getting trained on more and more, but also assumptions or guardrails that need to be provided. Overall the 'action' stage is the most important phase to consider. Up to here you have created zero business value, even the most perfect data lake doesn't add any value, if no specific actions can be derived from it. When building out your roadmap, start with the outcome of what you'd like to act on, and then work backwards on how to achieve it.

Data Audits: Periodically run audits to ensure that the data collection methods are still aligned with your business goals. This way, you're not collecting data that no longer serves a purpose.

Additionally, fostering a culture of data-driven decision-making across departments ensures everyone understands and values the data being collected. Encourage departments to share insights. Often, a pattern observed in marketing might provide valuable insights for customer service and vice versa.

Different channels have very different margins. You should be able to measure this and act accordingly. Sometimes a low margin product should not be sold on marketplaces, or a high volume item should be with different offers and discounts on each channel. Calculate your margins specifically on google shopping offers and marketplaces.

Use special tools to keep an eye on social media. These tools help you see when people talk about your brand, how they feel, and what topics are popular. This information is really useful for planning your marketing. It's like having ears everywhere to know what people are saying about your business.

The global nature of e-commerce means that data privacy laws and regulations vary across countries. GDPR, CCPA, etc., can have implications for data collection. So, while collecting data, always ensure you're compliant with international data privacy standards to avoid potential pitfalls or penalties.