We’re familiar with the images – long lines of people huddled on the sidewalk outside big-box stores, viral footage of stampeding bargain shoppers. For consumers and retailers, these are the telltale Black Friday signs that the holiday shopping season has truly begun.
This week, shoppers are getting ready both on and offline for one of the largest shopping events of the year. While Black Friday is undoubtedly a huge day for merchants, how does its sister holiday, Cyber Monday, compare?
Cyber Monday is the relatively new kid on the block. Still, it’s rapidly gained traction as another strong day for online deals and promotions. We wanted to see exactly how big a difference there was between the two shopping holidays last year, and understand if there are potential opportunities for retailers to optimize their spend.
Large spikes in consumer engagement and advertiser spend are characteristic of these two holidays and last year was no different. In 2014, Black Friday saw clicks and budgets sharply spike upwards of 180% of November’s monthly average. This all resulted in 200% more conversions than an average November day for retailers before declining to near-average levels.
While Black Friday performance was impressive, Cyber Monday shot it out of the water. On Cyber Monday, clicks and spend rose to over 200% and 275%, respectively. Conversions rose an astounding 300% on Cyber Monday.
While Cyber Monday may be the online leader in retail sales this month, this doesn’t mean that retailers should neglect Black Friday. Cyber Monday is exactly that, cyber and online, and shoppers who are buying in-store are a valuable segment that shouldn’t be discounted. And, many of them are relying on online resources to augment their in-store experience. For retailers, both of these shopping days will be crucial for a successful holiday season, and attributing spend accordingly is a must for the successful marketer.
With the recent release of Google’s Customer Match, the ability to target users through their email address has finally come to search advertising. This type of targeting has been available in social since Facebook announced Custom Audiences in 2013, and is accessible to display through data onboarding. Now, because of Google’s new feature, advertisers can target users using this data across search, social, and display, and across multiple devices.
This opens up many new possibilities for cross-channel, cross-device advertising. As it stands, a large percentage of marketing CRM emails are never opened. Advertisers can’t depend on email alone to connect with high-value customers in a CRM. We recommend using your CRM data to serve ads across search, social, and the web.
First, some background. The deterministic matching method relies on personally identifiable information commonly stored in CRM systems. With this method, a linkage is made when a user in your CRM uses the same email address or social media user IDs to log into an app and a website – across browsers and devices.
As long as a user is logged in across devices and targeting is set up across channels, advertisers and publishers can use this unique identifier to target those users cross-channel, on multiple devices.
Google, Facebook, Twitter, and Display Networks already allow you to serve ads to previous site visitors with remarketing lists. This is traditionally done with cookie pools. Customer Match, Custom Audiences, and display customer targeting all allow you to advertise to recognized, signed-in users wherever they are – whether it’s mobile, tablet, laptop, or desktop.
This cross-channel path is difficult for cookies to traverse. It’s also hard for cookies to move across different browsers, and users can easily delete most cookies.
The other main advantage is that CRM data can be collected from multiple offline sources. For example, retailers can ask for a customer’s email address after an in-store purchase, or a travel agent can ask for an email address after a phone booking is made.
1. Do the Right Thing for the Right Channel
When it comes to matching CRM data with users for targeting, each online advertising channel has slightly different options. Be sure to make the most of each channel’s unique possibilities.
Google’s Customer Match is a new product designed to help you reach your highest-value customers on Google Search, YouTube, and Gmail. Customer Match allows you to upload of a list of email addresses, which can be matched to signed-in users on Google in a secure and privacy-safe way. From there, you can build campaigns in Marin with highly relevant targeting and specifically tailored messaging for your audience.
Email lists, phone numbers, Facebook user IDs, Twitter IDs, mobile advertisers IDs
Custom Audiences (Facebook) and Tailored Audiences (Twitter) make it easy to target specific customers or prospects at scale. It allows you to match your customer list against Facebook, Instagram, and Twitter users in a secure and privacy-safe way. Advertisers can use Marin to target users across social platforms and devices.
Email addresses, CRM, point of sale, and mobile advertisers IDs
Through uploading emails, CRM data, point of sale, and mobile advertisers IDs, data onboarding technology (such as LiveRamp) can match your anonymized data to online devices and digital IDs, and segment audiences. These audience segments can then be sent to Marin for display targeting.
2. Be Sure to Segment
Segmentation is key to the success of CRM targeting for search, social, and display. Users can be segmented by value, actions, loyalty, recency, and satisfaction, among many other options – the segmenting possibilities of your customer database are virtually unlimited. You can use all of these segments for innovative advertising, such as enhancing your strategy, target audiences, and creative based on fresh and reliable data.
3. Go Cross-Channel
Using CRM data for targeting can produce fantastic results in single-channel siloes. However, when it’s used as part of a cross channel marketing strategy, the number of creative marketing tactics becomes almost limitless.
One common example of using CRM data across channels is targeting users with tailored messages across search, social, and display, depending on whether or not they’re existing customers.
Channel exclusion lists are just as important as positive targeting lists. In addition to reaching specific audiences with your ads, you can exclude unprofitable channels but still reach the same audiences.
For example, suppose an advertiser is in an industry where search keywords are particularly expensive. But, they want to update existing customers about a new product in a more cost-effective way. They could exclude the existing users from search targeting but still advertise to them on social and display.
CRM targeting strategies also open up new customer care and support avenues outside of phone, email, or direct mail. If a customer has a specific issue, it can be resolved at the level of a search query. Using CRM data, you could automatically deliver the most relevant information and links based on the products or services your customers are using, even if they use the exact same search query to search for information.
Using CRM data and user matching addresses a number of the challenges of cookie-based remarketing. It also helps bridge the gap between offline and online marketing activities. With Google’s new Customer Match, CRM data can now be used to actively target across search, social and display. This paves the way for innovative cross-channel, cross-device advertising strategies.
In just a year, display has gone from a desktop-based ad channel to a mobile one, showing a dramatically faster shift than either search or social. Not only has the display advertising world seen huge changes this year, but even more changes are anticipated in 2016.
This is indicative of a larger trend in digital advertising as a whole, where consumers are spending more time and attention on mobile devices like smartphones and tablets instead of desktops. In response, advertisers are allocating more and more of their display budget to targeting mobile consumers.
During Q3 2015, consumer engagement with display ads moved very decidedly towards smartphones. Over half of all display ad clicks came from a smartphone, and these ad clicks resulted in the majority of conversions.
eMarketer predicts that, by end of year, 60.5% of display ad budgets will be on a mobile device, and we’re seeing the same trends within Marin. This added consumer attention has translated to heightened innovation in the mobile display ad space. New formats for display ads are coming out on a regular basis, replacing the old banner ads to help encourage more click-through and conversion on mobile display ads.
For more information about the current state of display advertising and forecasts for 2016, download our report, The Q3 2015 Performance Marketer’s Benchmark Report, and check out our industry infographic below.
On the heels of Google’s success with Product Listing Ads and Shopping Campaigns, other publishers have developed their own product ad platforms, most notably Bing with its Product Ads and Facebook’s Dynamic Product Ads. These major players have proven that shopping ads are a viable and highly effective marketing investment for digital advertisers.
And, shopping ad campaigns have now been around long enough for us to identify exactly which trends retail advertisers should be most aware of.
In 2014, advertisers spent a whopping 318% more on product ads in December than they did in January of the same year. Share of clicks closely mirrored this spend, with one in four paid-search clicks being on a product ad in December of 2014.
It’s undeniable that this trend will continue, as spend continues to increase, product ads get more sophisticated, and advertisers continually optimize for the most aesthetically pleasing and persuasive ads.
Again, the holidays are always a boon for digital advertisers in the retail space. During Q4 of the last couple of years, spend on shopping ads pulled ahead of text ads, since retailers served engaging and eye-catching ads during that time. What’s surprising, however, is that this year, text ads have an advantage.
Since mid-year 2014, text ad CTR has actually increased, and overtook shopping ad CTR until the end of the year.
Not only are we experiencing a mobile revolution – there is a groundswell of mobile ad clicks. Even though desktops had a 25% increase in clicks during November and December of 2014, this pales in comparison to smartphone’s almost 90% increase between January and December. During the holidays, consumers are now shopping and clicking at the same time, on screens uniquely designed to offer ads, deals, and product information to someone on the go.
For more information and data charts on Google and Bing shopping ad 2014 performance, download our new report, Google and Bing Shopping Ads Report: Current Trends and What Lies Ahead.
With Google processing more than 3.5 billion search queries in a single day, there’s a surprising amount of insight that can be gained by analyzing the content and user behaviors behind these searches. Studying your own paid search data can substantially benefit several areas of your marketing strategy, including product, pricing, competitive strategy, branding, and store location selection.
Search engines periodically release insights based on overarching or vertical-specific search trends. Bing and Google both offer places to start researching search trends related to your product. This data can help you:
If you analyze search query data directly associated with your brand name, you can identify customer pain points, bugs, and potential new product features.
SEM ad creative testing can help inform pricing strategy, as well as the best way to message pricing.
The search engine results page is a goldmine of competitive data. By performing searches related to your product, you can:
Even if you don’t plan to launch a competitor campaign, it’s important to monitor your own branded search terms. The simplest way to do this is to look for spikes in your brand CPC. If it jumps, you may have a new competitor.
Search data can help you test new approaches to brand messaging. Marketers can utilize search data to:
Search data can help inform your store location strategy by providing:
Search marketing isn’t just a direct response channel, but rather a means to inform your marketing strategy with data. Product, pricing, competitive strategy, branding, and store location selection are just a few examples of how to use search data. Make it a regular practice to analyze search data for insights that can be applied throughout your marketing strategy.
Sarah manages Content Marketing at Boost Media and leads a team of marketing professionals to drive revenue through complex B2B marketing campaigns in the ad tech industry. Prior to joining Boost, Sarah developed marketing and sales strategy at BNY Mellon, a top 10 private wealth management firm. In a former life, Sarah worked in journalism writing for magazines including Boston Magazine, The Improper Bostonian, and Luxury Travel. When she’s not writing engaging content, Sarah enjoys cooking, running, and yoga.
Boost Media increases advertiser profitability by using a combination of humans and a proprietary software platform to drive increased ad relevance at scale. The Boost marketplace comprises over 1,000 expert copywriters and image optimizers who compete to provide a diverse array of perspectives. Boost’s proprietary software identifies opportunities for creative optimization and drives performance using a combination of workflow tools and algorithms. Headquartered in San Francisco, the Boost Media optimization platform provides fresh, performance-driven creative in 12 localized languages worldwide.
Click here to schedule a free demo of the Creative Optimization platform today.
As a performance marketer, there’s never a day when I’m not using an analytics platform to measure the performance of my campaigns. It’s extremely important to track and measure advertising spend and understand how people are engaging with the assets you’re promoting. If you’re looking for a way to prove the effectiveness of your marketing efforts, then you’re in the right place. In this post, I’ll walk you through how to set up and define goals and conversions, tag your advertising campaigns for consistency, and analyze the data for actionable insight on Google Analytics.
Goals are essential, allowing us to see conversions, conversion rates, and conversion values. From making a purchase to downloading a whitepaper or requesting a demo, tracking goals is critical in evaluating the quality and value of traffic to your website. It’s important to measure different segments, understand how people are engaging with your site and content, and see what the outcomes are.
Now that you have goal tracking figured out, the next step is to tag all of your advertising efforts with consistent landing page parameters in order to get a clear view of what channels are most effective. For a basic understanding of how this works, please refer to Google Analytics’ URL builder. There is no absolute way to do this and it depends on how you want to structure your advertising for ease of reporting, but once you develop the model, it is important that you stick with it. Here’s how I apply the utm parameters in order to understand performance for Marin.
No matter what you decide to put in these parameters, it is very important that you use the same naming convention throughout. If you don’t have a clear understanding of how to use utm parameters consistently, it will get very messy and be very difficult to understand your data.
Example Scenario: I need to promote our 2015 Mobile Report on various channels – Facebook Ads, Twitter Ads, and Linkedin Ads. This is how I would setup the landing page utm tags per channel based on the type of advertising that’s running.
Do you see the consistency? The campaigns are all labeled the same naming convention, 2015Mobile Report, for all channels. The source and medium are the same labels that I’ve used throughout time for all marketing initiatives. For the content parameter, I use the ad name if the channel relies on a banner, otherwise I use it to evaluate groupings of my target audience.
Let’s do a quick dive and put these consistent tracking parameters into action by analyzing the data using the tags indicated, in addition to looking at goal completions to see how many people converted.
Example Analysis: Taking a look at each source/medium for the 2015 Mobile Report campaign, I can see that most of the website traffic and conversions are coming from facebookwca and linkedin-su. I can back into the cost per conversion by getting the advertising spend. So, say that I spent a $1,000 on facebookwca and $1,000 on linkedin-su, the cost per conversion in this scenario would be $16.13 ($1000/62) for facebookwca and $17.24 ($1000/58) for linkedin-su. As for the other campaigns, I expected the low results because they are new campaigns that were recently set live.
To do a high-level source/medium analysis, navigate to: Acquisition > All Traffic > Source/Medium.
To do a high-level content analysis, navigate to: Acquisition > All Traffic > Source/Medium, then click on “Other”, expand “Acquisition” and click on “Ad Content”:
The steps for analyzing both are similar to the steps indicated in the campaign analysis – you simply type in the parameters you used in your URLs and your data will populate.
If you have any questions, feel free to leave a comment. By spending time analyzing your marketing campaigns, you are one step closer to becoming a performance marketer.
In the previous post we discussed how to segment your customer base for lifetime value (CLV). In our fourth and final post of this lifetime value series, we’ll provide some recommendations for optimizing for CLV.
Once you have identified audience segments, you’ll want to investigate things like:
The idea of optimizing for CLV is to achieve increasing profits from your existing customer base, or find new high-value customers.
Be channel agnostic when looking for marketing opportunities
When asking these questions, it’s useful to take a channel agnostic approach to your thinking. Instead of relying on data from one specific channel, you’ll want to incorporate data signals across different marketing channels like search intent, behavioral data, and audience characteristics, in addition to your own data, in order to paint a detailed picture of your high value segments and develop strategies to increase your customer CLV.
Once you have a good understanding of your high-value customers, you can start developing strategies to drive higher CLV. A few potential scenarios follow below:
Scenario 1 – Find new high CLV customers
A financial company could improve its acquisition efforts by refocusing its spend towards an acquisition channel that has proven to attract high CLV customers. Alternatively, it could also expand its reach by leveraging 2nd and 3rd party data to create look-alike audiences for additional targeting opportunities.
Scenario 2 – Reduce customer churn
An insurance company creates an audience segment for current customers with contracts expiring within the next 30 days in order to run a search retargeting campaign that ensures the brand has top that the brand is well represented when the user conduct relevant searches.
Scenario 3 – Increase repurchase rates
An online clothing retailer rolling out a new seasonal line could remarket its product ads on Facebook to customers who haven’t bought from the company within the past 6 months.
Scenario 4 – Cross-promote or upsell to potential customers
A home improvement retailer could look for opportunities to cross-sell to potential customers who’ve recently shown interest in home loans. Alternatively, the retailer could promote upsell opportunities to sell kitchen upgrades to customers who’ve recently indicated interest in or purchased a new appliance.
The possibilities are numerous. By taking basic customer lifetime value information, developing useful segments, and connecting that information with the customer data that you’ve collected or have access to, you can open up a number of new, interesting ways to make smarter marketing decisions and increase profits.
Looking for additional posts in this series?
3 Reasons Why Segmentation is Necessary to Understand Customer Lifetime Value
Gathering Customer Lifetime Value Data – Start Small and Build
Using Lifetime Value to Create Audience Segments
Now that you have your data in order, you’ll want to start parceling out relevant customer segments.
The goal is to identify segments that are practical and helps you make better marketing decisions. You’ll want to strike a balance between segments granular enough to execute targeting strategies, but broad enough to scale your targeted marketing efficiently.
There’s no shortage of advanced statistical models to determine customer segments, but in this post we’ll provide a few straightforward, practical recommendations.
Start segmenting based on customer value.
The 80/20 rule, which states that 80% of your profitability comes from 20% of your customers, is a good place to start your segmentation effort. You might actually find your customer profitability distribution is even more extreme with over 100% of your profits coming from your top customers, and your worst customers actually taking away from your bottom line. However, at the most basic level, the distribution might look like the above graph.
In this case, we’ve identified high CLV, medium CLV and low CLV segments. However, these segments are still too broad for much use. From here, you’ll need to rely on your data to help unearth demographic or behavioral commonalities within each segment.
Ideally, you’ll want to try to identify segments with commonalities that you can act upon and measure changes in performance.
For example, you might find that:
Once you’ve identified different customer segments, you can identify opportunities to optimize for your higher CLV segments. We’ll explore some ideas to optimize for lifetime value in our next post.
It’s important to segment your customer data to gain useful insights, but the relevant segments may not become apparent until you start wading through the financial data. In this post, we’ll discuss three important financial factors you’ll need for your customer lifetime value (CLV) calculations.
There are three important things you’ll need to find:
1. Retention rate/Customer lifetime
2. Revenue per customer segment
3. Margins per customer segment/sale
The latter two are pretty straightforward, and don’t leave much room for interpretation, so in this post we’ll focus on divining the retention rate. (One caveat on revenue – businesses with a brick and-mortar presence should also make sure to account for the “research online, buy offline” effect when calculating CLV. Otherwise,customers acquired via online channels will be undervalued.)
Customer “lifetime” doesn’t mean forever
“How long is a customer’s lifetime?” is a pretty common question. The “lifetime” in lifetime value isn’t quite so literal. You’re not expected to be projecting profits for the 75 years or so of a customer’s life. For one, that’s impractical. But more pressingly, the further out the revenue projections, the less reliable they are. Generally, we would recommend calculating your CLV within a five-year window (although some exceptions apply).
There are a number of different ways to figure out your customer’s lifetime. Two of the more common methods involve using your retention rate or your repeat purchase rate.
Scenario 1: Use retention rate
If you charge your customers a monthly or yearly retainer or subscription fee, you should know your monthly or yearly churn rates (retention rate = 1 – churn rate).
For example, an enterprise SaaS company with a yearly retention rate of 90% would have an expected customer lifetime of 10 years based on the equation below:
Scenario 2: Use repeat purchase rate
Repeat purchase rates are very similar to retention rates, except they are more relevant for companies whose customers have irregular purchase cycles. For example, a retailer with repeat purchase rates of 50% will have different customer lifetimes depending on whether their customer buys from them every 3 months, or every 3 years.
Scenario 3: You know neither
Some businesses may not know what their repeat purchase or retention rates are. In those cases, you’ll have to back into the number. You can do this by specifying a gap of inactivity where you decide a customer is no longer a customer, and then measuring the time period between the customer’s first and last purchase.
The period of inactivity in which you determine a customer has lapsed depends on the nature of your business. For example, a grocery store whose best customers buy weekly might determine that a 3-month dry spell is sufficient enough to indicate that they are no longer customers. Other businesses – auto dealerships for example – will have much longer lag between purchases.
Don’t forget about your segments
Regardless of how you calculate your customer’s lifetime, remember from our previous post that you’ll want to find the customer lifetime for each relevant segment you’re measuring, not just the total average customer lifetime.
This is part of a 4-part blog series. Missed the first post? Read 3 Reasons Why Segmentation is Necessary to Understand Customer Lifetime Value here. Our next post will dive deeper into how you can carve out relevant customer segments, so stay tuned!
Marketing metrics like CPC or CPA only capture the cost part of the profit equation. This seems a bit odd when you think about how often successful marketing campaigns are judged on the revenue portion of the equation. It’s also detrimental over the long-term because it treats your marketing efforts as a cost-center vs. a revenue center.
Incorporating customer lifetime value (CLV) is important because it takes both revenue and costs into account. Our recent white paper provided an introduction on how to introduce customer lifetime value into your online marketing campaigns. This post is the first of a 4-part series that will provide pragmatic recommendations for building lifetime value models.
Prelude – Get your data ready
A lifetime value model is only as good as the quality of the data you keep. To develop a good CLV model, you’ll need to ensure you’re accurately measuring things like revenue per customer, margin per sale, and retention/churn or repeat purchase rates.
Once you have your data, there are three good reasons why you need to segment your customers to get an accurate understanding of your customer lifetime value.
Reason 1: Average Revenue Per User (ARPU) is incomplete
A simple example can illustrate. Let’s say a business has two customers:
Customer #1 is an unmarried, 25+ y/o librarian, $35K HHI, and buys a hatchback for $15K. Customer #2 is a married, 55 y/o executive with 3 kids, $250K HHI, and bought a sports car for $160K.
Based on this data, the average customer for this business buys $80K cars. Except in reality that $80K car is far outside the price range of Customer #1, and it might not be upscale enough for Customer #2. Furthermore, the average customer’s demographic info based on the details above is basically worthless.
This is why segmentation matters. ARPU tells you that you have paying customers, segmentation can tell you who’s actually driving your business.
Reason 2: Focus your effort on the segments matter most
Go back to the two customers above. Which segment appears to be more valuable to you? Which is the one you want to focus your marketing on?
That’s a trick question. The answer is it depends. If you can convert the hatchback buyer and she buys several cars from you over her lifetime, then she could be worth more than the sportscar buyer. But if you were focusing on a window of 3-5 years, then you’d probably want to focus your marketing efforts on other sportscar buyers.
Either way, you can’t make this decision until you know how different your customers are.
Reason 3: Make better decisions
You might choose to target both customers. And that’s where segmenting for CLVcan be most helpful. Once you can divulge a segment’s CLV, opportunities for acquisition, upsell, and cross-promotion may become more visible. For example, you might leverage retargeting campaigns to upsell to segments with high CLV. Alternatively, you may find that there are cross-promotion opportunities with a segment with lower CLV but potential for growth. Finally, you might decide to cut marketing in channels that consistently acquire customers with low CLV.
Ultimately, understanding your customers’ CLV enable you to be a better marketer. This is a 4-part blog series, so stay tuned for more best practices to follow! [Part 2: Gathering Customer Lifetime Value Data – Start Small and Build]
Marin scored the highest overall G2 Score on G2 Crowd’s Winter 2016 results. Learn more about this important win! bit.ly/21g4EJS