This is the final installment of our three-part blog series in which we identify some of the biggest challenges for today’s online retailers and discuss how you can overcome them. If you haven’t already, read Part One: Attaining Customer Loyalty in a Competitive Environment and Part Two: Leveraging New Trends in Artificial Intelligence.
Part Three: Handling and Analysing Big Data to Maximise Growth
“The goal is to turn data into information, and information into insight.” - Carly Fiorina, Former CEO of Hewlett-Packard.
Remember in school, learning the difference between data, information and knowledge? A quick refresher:
- Data is the raw facts of the world, such as figures, percentages and other unprocessed information with no context to explain its meaning. Example: “Teacups sold: 130. Teapots sold: 2.”
- Information is when we take the data and make conclusions from it. Example: “We sold many teacups this week, but nobody really wants to buy teapots.”
- Knowledge is the judgement and experience we can apply to the information to make it useful for us. Example: “We should try running a promotion to increase sales of teapots.”
In this blog post we’re going to take a look at where you can gather data from, the steps needed to turn that data into useful information, and how to glean new knowledge from this information and action it to maximise your marketing ROI.
Specifically, we’re talking here about big data. And when we talk about big data, we mean large amounts of complex data that could help you to identify patterns, trends and associations and act upon them to increase the growth of your ecommerce business. Big data is identified through the “three Vs”: volume, velocity and variety. It’s large amounts of data that is received at a fast rate and comes in a range of unstructured forms. Find out more about the history of big data, its general application and best practices at Oracle.com.
What is the Role of Data in Helping Marketers to Get Ahead?
So, in terms of online marketing, what are some examples of big data? We could talk about data in terms of areas such as:
- Managing customer data for:
- Cross-device targeting
- Linking online and offline activity
- Data-driven advertising
- Personalisation in email marketing
- Managing large product inventories for:
- Shopping ads
- Making promotion and sales decisions
Information you hold on your customers can help you to deliver more personalised PPC, social media and email marketing. It can also help you to reach customers when they move between devices, and map online activity with offline store visits and purchases to give you a complete picture of the customer journey. Your product information can be used to create automated shopping ads containing details specific to your customers’ needs, and it can help you to make better sales decisions. We’ll dig into all of these in more detail later.
Why is big data a challenge for online retailers?
The challenge of big data is in the name – when you have a massive amount of data that’s in a variety of different forms and continuously changing, how do you organise it and make enough sense out of it to formulate an effective data-driven marketing strategy? Many businesses struggle to keep up with the changes to their data and the best methods for handling it. Especially when your data is evolving, it can be difficult to make use of it because in order for it to be useful it needs to be clean, accurate and organised. And that’s before we even consider the privacy and security implications of gathering and handling customer data. For online retailers in particular, your data could be of great value to you, and if you’re not using it as part of your marketing strategy you could be missing out on a lot of opportunities. Meanwhile, your competitors likely also have a lot of customer and product data they may be using to their advantage – and to your detriment.
However, as well as being a challenge, when used well, big data can offer great benefits. It can help you to make better decisions based on large amounts of valuable and reliable data, giving you more confidence in your strategy. Particularly for machine learning strategies such as those used in Google Ads for smart bidding and custom audiences, the more data available, the better decisions Google will make based on this data.
Getting a Full Picture of the Customer Journey
Where does customer data come from?
You can use customer data to investigate many aspects of your online presence. This data might come from:
- Your online orders
- CRM software such as Salesforce
- Email marketing lists
- Google Analytics
- Google Search Console
- Heatmapping software such as Crazy Egg
- Social media such as Twitter Analytics and Facebook Analytics
This is not an exhaustive list, but it shows that there are many different places you might get data from, and they all serve a different purpose. Knowing what to do with data from each source and how to put data together can help you to identify new opportunities, assess the effectiveness of current marketing efforts, and develop a complete data-driven marketing strategy.
The customer journey is no longer seen as a straight line from point A to point B, but in fact leaves room for a lot more wibble-wobble and back and forth. Especially with a larger, considered purchase, customers may research for a prolonged period of time, through several websites and using multiple devices. A purchase decision that starts out on a phone may be completed on a desktop, and vice versa. In Google Analytics you can now see when users visited your website from two different devices. You could use this information to decide how you are going to market to users on each device. For example, if users tend to carry out initial research on their smartphones but later convert on a PC, you may want to adapt your marketing strategy to target those searching for more informational keywords on mobile, but ramp up your PPC with strong calls to action for desktop users.
When it comes to reporting on where traffic and conversions have come from, it’s important to choose an appropriate attribution model. Historically, the default has been a last-click attribution model, which means that the last touchpoint gets the credit for the sale. For example, if a customer clicks on a PPC ad, then clicks on an organic search result and then converts, the organic link would get all of the credit and the PPC ad would get none. To see how each touchpoint has contributed to a sale and to get more accurate data on what is driving your conversions, choose a different attribution model. This also works across multiple devices. Consider the data-driven attribution model, which uses Google’s machine learning to distribute conversion value based on the whole customer journey. Find out more about attribution modelling.
Linking online and offline activity
To further help you complete the big picture, consider how you can link online and offline activity together. If you have a bricks and mortar store and an ecommerce site, a challenge for you may be that while you can easily view and analyse data and purchases online, you’re struggling to see how your online efforts are translating to footfall. It would be helpful for you to be able to see how many store visits you can map back to online starting points, and whether it was PPC, email or something else driving those visits.
There are a few different ways for you to link online activity with offline. You could:
- Use Google store visit conversions, which looks at users’ phone location history to determine whether they visited your store and aggregates this anonymous data
- Import offline conversions into Google Ads from your CRM
- Create an offer which requires a voucher or QR code to be used so that the customer has to hand in something physical when they visit the store, which can be linked to an online campaign
- Use unique URLs in print or offline campaigns, which redirect to landing pages on your website when entered
- Require visitors to your premises to sign in using a digital method, such as Google Forms, entering an email address or membership ID
By gathering more data about your customers’ visits and offline purchases, you can build a better picture of your overall marketing efforts, and see where you could improve both online and offline.
Enhancing the Customer Experience
As we discussed in part two of this blog series, audience targeting in Google Ads can help you to reunite with previous customers or reach out to new audiences similar to your current customer base. Audience tools include affinity audiences, customer match, similar audiences, and remarketing audiences. The more data Google has to work with in your PPC account, the better it is placed to suggest new users who could turn into customers. You can find out more about audience targeting in our blog post “What is Audience Marketing – And Why is it Key to Your Digital Growth”. Audience targeting is a great example of how you can gather data and use it to increase your conversions, as well as to show your ads to users who are more likely to be interested in your products.
Moving away from PPC for a moment, let’s think about how big data can help you to create a more personalised experience for your customers. If you have a huge email marketing list, segmenting your audiences by demographics or interests can help you to send them more relevant information. For example, a clothing retailer selling men’s, women’s and children’s clothing wouldn’t want to send emails about all of its products to everyone. You could ask your customers what they are interested in when they sign up to your mailing list so that they can tailor communications to be exactly what they have asked to hear about. Or you could send emails recommending products based on a customer’s previous purchases. In this way, you can drill down into your big data to find the nitty-gritty details that will help you target users more directly, making them more likely to convert with you.
Big data can also help you to improve your customer service efforts, which is another way of standing out from your competitors. By keeping data from previous transactions, as well as customer preferences, and making good use of your CRM, you will be better equipped to handle issues should they arise, and to give customers an experience tailored to their needs and interests.
Of course, we can’t mention customer data without mentioning GDPR. Data security is the foundation of trust between you and your customers, so it’s essential that any data you hold complies with these regulations, that customers have consented to you holding their data and using it to communicate with them in ways they are happy with, and that you adhere to any requests to know what data you hold, and to remove that data if necessary. It should also go without saying that any data you hold should have been gathered legitimately, and not through methods such as buying email addresses.
Using Your Data to Enhance PPC Campaigns and Influence Marketing Decisions
Let’s turn now to the data you have about your products. Large retailers can have a huge inventory, which paves the way for a wide range of opportunities with Google Shopping Ads. With Shopping Ads you can upload a product feed into Google Merchant Centre, and this feed will contain important information about each product, including the title, price, brand, description, link, image link, availability, sale price, product type, condition, size, and more. But it isn’t simply a case of telling Google what you sell and creating ads for each product. When you use a product feed, your ads will update automatically so that the information in your ads is always up to date. So if you change the name or price of a product for example, your ads will reflect this. You can also set up your ads to stop showing for products that have gone out of stock and to start showing when they are available again, as well as to automate creation of ads for new products. You can also dynamically insert product attributes into ads using Ad Customisers, so that each ad is tailored to exactly what the user is searching for. Find out more about shopping feeds in our blog post Improving Large-Scale PPC Account Management with Feed-Based Automation.
Making sales and marketing decisions
Keeping a handle on your product inventory is also important for making sales and marketing decisions, especially when paired with competitor research. For example, what are you struggling to get rid of and how could you promote it to reduce its space in your inventory? What is your competitor not offering that you could provide for people who are looking for it? Should you be selling you excess stock in bundles? The data on the stock available to you should help you to improve your marketing strategy. What have you sold a lot of, and through what channels? Which marketing methods seem to be working for one product, and will they work for another, or do different product types require different strategies? Your inventory could be a goldmine of information if you know how to dig into it.
What Challenges Will the Dawn of a New Decade Bring?
In this blog series we have discussed three big challenges for online retail: gaining and retaining customer loyalty, keeping up with trends in AI, and leveraging the power hidden within your big data. The next new year will bring a new decade, and likely a whole new host of different challenges for retailers. Thinking back to nearly ten years ago in 2010, promoted tweets in Twitter were brand new, people were excited about being able to check into locations with Foursquare, and virtual assistant Siri was yet to be born. Ten years is a huge amount of time in digital marketing, but we’re excited to see what new innovations are on the way and how we can continue to help our clients navigate their challenges and grow their online businesses.
We hope this blog series has offered some insight into how you as a retailer can use a variety of digital marketing tools to grow your business.