It takes effort to lead a casual observer all the way through the customer journey to become an actual revenue-generating client. If you’re struggling with this process, you should consider implementing omni-channel marketing strategies. These methods can make it much easier to attract and encourage buyers through the customer journey.
What Are Omni-Channel Marketing Strategies?
Omni-channel marketing strategies involve interacting with clients through multiple channels. The channels share customer data, ensuring consumers receive content reflecting their prior interactions. Essentially, each interaction builds upon the previous one, creating a more cohesive and consistent customer experience.
For example, suppose that a customer searches online for a new pair of Hoka running shoes. Their search turns up dozens of options, and they click on a pair that connects to your website.
Once on your site, perhaps they read the product description and check the price but ultimately don’t make a purchase. Later, you could target them through Instagram and share an ad for the same pair of Hoka shoes with a 10% discount. Perhaps they’ll click on the ad and buy the shoes, generating a sale for your business.
According to an Omnisend study, omni-channel marketing strategies lead to an 18.96% higher engagement rate. In comparison, single-channel marketing campaigns have just 5.4% engagement. It’s therefore worthwhile to adopt omni-channel strategies when working to build a solid customer base for your business.
Data Holds the Keys to Success for Your Omni-Channel Marketing Strategies
Creating and implementing an effective omni-channel marketing strategy takes work. You’ll need to carefully analyze the data you obtain from clients to understand how they interact with your ads and other content.
When using these strategies, you no longer analyze metrics from individual channels, like social media interactions or your website. Instead, you’ll need to combine data across channels to understand how clients move through the customer journey.
Combining data effectively starts with attribution models. An attribution model helps you determine which channels contribute the most to a sale. Using the results, you can optimize your omni-channel marketing strategies to capture more leads.
There are two primary types of attribution models: single-touch and multi-touch. Single-touch attribution models are easier to create but may not share the full story of the customer journey. Multi-touch attribution models are more complex to implement, but they provide more information.
Single-Touch Attribution Models
Single-touch attribution models assign one specific channel to the revenue generated from a purchase. A first-touch attribution model recognizes the client’s first interaction with your company as the source of the sale.
For instance, if the first record you have of a customer comes from a click on a Facebook ad, you would credit the sale to that ad. You would do so even if they had multiple other interactions with your brand before finally making a purchase.
A last-touch attribution model takes the opposite approach. Instead of crediting the client’s first interaction with your brand, it assigns the sale to the last channel they connected with before purchasing from you.
Suppose that the customer saw multiple social media ads, signed up for your emails and texts, and finally purchased something after receiving a special offer via SMS. Under the last-touch attribution model, the SMS text would receive all the credit.
In a last-non-direct click attribution model, the marketer apportions the sale source as the final channel of communication before the customer directly visits your website and makes a purchase. For instance, perhaps they saw an ad on connected TV and then decided to visit your website and buy something three days later. In this case, the connected TV ad would receive all the credit for the purchase.
Multi-Touch Attribution Models
Another option to consider is the multi-touch attribution model. Under these frameworks, multiple channels that your clients interact with prior to their purchase receive some type of credit for their sale.
In the linear attribution model, every channel interaction receives evenly distributed recognition for the sale. For example, the customer might see two ads on social media, receive an SMS text, and read a blog post from your company before making their purchase. Since the client had four interactions, each one would receive 25% of the credit.
The time-decay attribution model recognizes the last few channel interactions before the sale rather than the entire communications chain. Most marketers look at the previous two or three interactions in a time-decay model and assign a weighted percentage to each one.
In a U-shaped attribution model, the first and last channel interactions receive most of the recognition for a sale. However, other channels also earn part of the credit — just on a lesser scale. If a marketer assigns 40% of the sale to the first and last touchpoints in the customer journey, they might then divide the remaining 20% evenly among the other interactions.
The algorithmic attribution model is highly complex and requires skilled data analysis. However, its results are considered to be more precise than those of other models. This model analyzes each touchpoint and assigns credit based on external and internal factors, like your company’s industry and sales cycle.
Techniques for Data Analysis
Before combining your data in an attribution model and using it to make marketing decisions, it’s critical to analyze it and remove any inconsistencies. For instance, if your website receives a lot of bot traffic, identifying and eliminating the bots from your visitor analytics will ensure that you only count authentic client visits rather than machine-generated ones.
After you’ve scrubbed your data from each channel, you can decide which attribution model is most appropriate for your business and then begin reviewing your results. Some models will be easier to analyze than others, especially those in the single-touch attribution category.
If you’re unsure which attribution model is appropriate for your organization, start by selecting multiple models and comparing the results. You could, for example, try a last-touch single touchpoint model and a time-decay model to determine which one yields the most strategic insights. The one that offers the best information should be the one you go with.
It is best to combine your data into one single location. You don’t want to be jumping between multiple platforms trying to measure customer interactions from each one. Google Analytics 4 provides cross-channel analysis in a straightforward interface. It can harness data from multiple online channels, including the following:
- Social media
- Websites
- Connected TV ads
- Email.
However, if offline channels like direct mail or print ads are a part of your marketing strategy, you’ll need to find a way to include them in your cross-channel attribution model.
Once you have a solid database of customer interactions, you can create buying profiles for your clients. You’ll likely see patterns among customers that will help you establish your customer personas. For instance, you may find that your younger customers find your organization through Instagram and TikTok, while older clients respond better to paid search ads.
Maximizing Your Omni-Channel Marketing Strategies for Optimal Impact
Of course, simply analyzing data in nicely curated attribution models won’t help unless you do something with it. Your buyer persona profiles should provide you with a lot of information, which you can analyze to identify the channels that lead to the most frequent sales.
From there, you can use those insights to devote more marketing attention to the channels that naturally encourage the customer journey. For example, suppose that you adopt the U-shaped attribution model for an e-commerce business.
Your analysis shows that female customers between the ages of 25 to 35 learn of your organization through Instagram ads. They have multiple other interactions with your website; most sign up to receive your promotional emails. The emails are typically the last interaction with your brand before they make a purchase.
Using that information, you could create Instagram ads to attract female customers in this age bracket. You might develop pictorials that show models of that age group wearing your company’s clothes or emphasize your marketing newsletter as a source of promotion and encourage followers to sign up for it. Once they do, you could send them a special discount they can use on any item in your store.
Optimizing your omni-channel marketing strategies will depend on several factors, including your industry, the types of customers you serve, and the channels you use. This will look different for every company.
Through Data Attribution Analysis, You Can Learn More About Your Customers
Data attribution models help you understand how customers interact with your business. They allow you to identify the most critical parts of your omni-channel marketing strategies and emphasize them in the customer journey. Once you select an attribution model for your company and analyze your data, you’ll understand which channels are the most critical to your client’s purchasing decisions.
