1. Set up the Facebook CAPI via code (not GTM)

You can set up Facebook CAPI either by directly putting the script into the website code with the help of your engineering team, or by setting it up yourself via GTM. The GTM setup instructions are easy to follow.

But installation via GTM limits the functionality of the Facebook CAPI, and you may think that the problem is with the service.

If you use the simplified setup with Google Tag Manager, you will have limited functionality for Facebook CAPI. Here you can learn more about the difference between Conversions API setup options.

2. Use special events

Special events are a great tool to optimize advertising not for default Facebook events, but for product metrics that will tie marketing activity to business indicators. Such a metric, for example, can be ROAS, ROI, LTV, and other.

Usually, setting up such events is difficult, but we have found the most comprehensive guide for you on how to set it up → Facebook’s instructions.

Do not extrapolate the value of the event, especially when calculating complex metrics. An example: the probability of a purchase after adding it to cart is 5%. A lot of marketers will consider that the value of this event is 5% of the price of the added products and send this data to Facebook. In fact, the value of this event differs depending on the user intent and other factors and should not be calculated linearly.

Essentially, Facebook’s task is to differentiate valuable visitors from low-intent ones. If you make such an assumption for each Add to cart action, you average out the value of the audiences and reduce Facebook’s ability to distinguish between them.

3. Add two fields to lead forms on you site to increase the match rate

Track ClientID and generate LeadID on the website (and not in the CRM), linking them together in analytics systems. This will let you link visits to the CRM records which significantly improves match rate.

Using the Facebook CAPI can improve the performance of ad campaigns manifold, and dedicating a few weeks to this is a profitable investment. If the standard set of Facebook CAPI tools is not enough or the transfer of non-default metrics is difficult in your product, it’s time to use Tomi.ai. Check the illustration in the header to see what stage you are at.

We have setup data transfer ourselves many times and often use these guides:

1. Compare Facebook CAPI to Tomi.ai
2. Facebook CAPI Developers Setup Guide by Meta
3. Setup Facebook CAPI. Video guide
4. Setup Facebook CAPI via GTM. Video guide

Сообщение 3 pro tips for setting up Facebook CAPI появились сначала на Tomi.ai.

To deliver the right message to the right person at the right time, you need to understand your target audience, their pain points, and what resonates with them. A/B testing is necessary to validate different messages and creatives, but measuring what matters is expensive and time-consuming.

Here are some tips to help you find the right message and creative for your target audience:

Measure What Matters

Messaging and creative directly affect every critical component that drives revenue, so it’s essential to get it right. Value-based optimization and monitoring is essential to drive not just the conversion rate but also the value of those conversions.

Use Generative AI Models

Generative AI models can help you produce different creative variants and flavors of the message. This can help you test more hypothesis per advertising dollar or unit of time.

Prioritize Your Hypothesis

There is no shortage of hypothesis, but you need to find the right message for each of your target audience personas and segments. You need to prioritize your hypothesis to test and find a way to test more hypothesis per advertising dollar or unit of time.

Measure Mid-Funnel Leads

Measuring quality leads or orders is critical but rare and expensive, so measuring mid-funnel leads could be a good compromise. You can measure the value of inbound leads and website traffic generated by your advertising creatives, and use this to optimize your messaging and creative.

Validate Your Results

Statistical significance is a measure of how sound your results are and how stable they would be in the future. You need to validate your results using A/B testing and measure the significance of your results.

Here are some useful resources to help you refine your messaging and creative to drive more revenue and ROI for your business:

1. Modern Guide to Lead Qualification by Clearbit
2. Data-driven Automation on Google and Facebook Ads by Webmechanix
3. Tomi.ai A/B Testing Calculator

In conclusion, finding the right message and creative that resonates with your target audience is a crucial part of driving revenue and ROI for your business. By measuring what matters, using generative AI models, prioritizing your hypothesis, measuring mid-funnel leads, and validating your results, you can refine your messaging and creative to drive more revenue and ROI.

To learn more about finding the right message and creative, watch our webinar on advanced A/B testing. We cover how to pick the right metric, maximize revenue and ROI, and validate your results.

Сообщение Advanced A/B-Testing появились сначала на Tomi.ai.

However, these traditional methods are what most advertisers are familiar with because they’re usually effective for understanding customers and optimizing paid ad campaigns. 

The good news is that there are better methods of optimizing your digital ad campaigns and boosting conversions without shifting your business model. You can better understand your prospects’ behavior and next steps through machine learning predictive models

How do advertisers measure digital ad effectiveness traditionally? 

Understanding how online ads perform has always been essential to the success of advertising campaigns. These three platform-related approaches are part of the conventional advertiser’s toolkit.

1. Attribution

Attribution is how we assess the ROI (return on investment) or value of the channels that connect businesses to prospects. It’s how a customer comes to know your business and makes a purchase. 

Attribution sounds simple when you think about what made the customer purchase from you. But customers rarely go straight to a website and make a purchase –– especially if your business has a long sales cycle like insurance or B2B SaaS.

Marketers usually choose between four types of attribution. 

i. First or last-touch attribution

Single source attribution models allocate all the credit to one touchpoint –– either the first or last touchpoint. 

First-touch attribution allocates all the credit to the first touchpoint the prospect engaged with. For example, an ebook download. Although it’s simple to analyze, first-touch attribution doesn’t acknowledge any customer interactions after the first touch. This means that any interactions with other channels following the first touch aren’t given importance. 

Last-touch attribution gives credit to the prospect’s final interaction before making a purchase. For example, signing up for a free demo or reviewing a brochure. But it doesn’t credit previous interactions like website visits, social media interactions, or calls with the sales team. This means you miss out on the importance of the channels leading up to purchase. 

ii. Linear attribution 

Linear attribution is a multi-touch attribution model that divides credit across every touchpoint in a customer’s journey. It recognizes every channel a business uses and provides a balanced look at the whole marketing and sales strategy rather than just one touchpoint. 

However, the downside is that it doesn’t show the different impacts of each channel. Businesses will find it challenging to identify the most or least successful channel. 

iii. Data-driven attribution

Data-driven attribution assigns credit for purchases according to how people interact with your ad campaigns and other marketing channels.

One major advantage is that it analyzes all the interactions that led to each conversion. This gives you a more holistic view of what converted visitors, unlike single-touch attribution. Knowing the full data story will help you improve your marketing and sales strategy since you can analyze each touchpoint. 

However, accurate results depend on having complete data. Google needs 15,000 clicks and 600 Floodlight conversions within the last 30 days to successfully generate a data-driven attribution model. 

That’s great if you’re an eCommerce retailer with a short sales cycle. But it’s probably impossible for businesses with long sales cycles like real estate agencies.

2. A/B testing

A/B testing forms a key part of optimizing paid ad campaigns. Your current A/B testing routine probably follows similar steps each time. 

When you A/B test, you likely create ads, allow the campaign to warm up, wait for a set number of conversions (perhaps 50), evaluate the results, and decide which ad performed better. You then continue to use the best-performing ad in your campaign, eliminating those that didn’t work out. 

This process works well for industries with short sales cycles, for example, an online grocery store. But for businesses with longer sales cycles, this method of A/B testing can take too long to yield definite ad performance results. If you’re selling property, it’s hard to wait until you’ve made 50 sales before A/B testing your ads!

3. Lead scoring

Lead scoring helps your sales representatives prioritize and organize their leads. Traditionally, lead scoring runs on two types of data: 

1. Firmographic – What we know about the prospect at the time of filling in the contact form
2. Product or CRM data – How the prospect behaved after the registration. Did they answer the SDR’s (sales development representative) phone calls? Did they engage with the product? 

This kind of lead scoring improves the sales team’s effectiveness.

But the predictions from both these data collection methods are too basic –– they don’t reflect the real buying intent of an individual. 

How does predictive machine learning boost your ad’s effectiveness?

When traditional attribution, A/B testing and lead scoring are enhanced with predictive machine learning, the results are nothing short of turbocharged ad effectiveness.

Predictive post-click attribution 

Predictive post-click attribution gives long-sales cycle businesses what they’re missing from other attribution models –– timely, accurate, and reliable info for data-driven decisions. 

Machine learning models analyze visitor behavior from each session and make accurate predictions about their next steps. Once the model is set up, results and scores are available in real-time. There’s also less need for having complete data –– it impacts the results far less than in data-driven attribution models. 

Forecasts present expected revenue for the next four weeks, too, so you’ll know when to expect busy and quieter periods of the year. Using predictive attribution, you can make your marketing experiments up to 30x more affordable –– that way, you’ll get the most ROI on your campaigns. 

Predictive conversions

Businesses with long sales cycles will find predictive conversions more effective. These make the whole A/B testing process 30-100 times faster. Instead of waiting on actual conversions that could take weeks or months to take place, you can make data-driven decisions about the effectiveness of ad campaigns more quickly. 

Predictive marketing relies on data provided by website analytics and customer behavior. When visitors land on your website, they receive a monetary score according to their behavior on your site. For example, we can calculate how much revenue a click on specific touchpoints will bring the business. 

Some touchpoints are more valuable than others –– a click on a demo page might bring you 1000 USD, while an ebook download might be worth 500 USD. 

Analyzing visitor behavior will help you decide which paid ad to run. You won’t need to wait for 50 or more conversions. Instead, you’ll have accurate predictive data to inform your upcoming paid ad campaigns. 

Predictive lead scoring

Technographic data does a better job of drilling down on potential buying behavior than its traditional counterparts, firmographic and product/CRM data.

Instead of estimating how prospects will behave as a result of their interactions with your business pre or post-registration, we score the lead’s behavior according to their interactions with your site or ads. 

Based on how they clicked around on different pages and took certain actions, it’s easier to determine whether they’re a red hot lead or someone just casually browsing. Every visitor is assigned a predicted purchase value. 

Understanding and analyzing the behavior of actual leads helps sales teams prioritize their leads. Predictive lead scoring can also help marketing teams determine who to target their ads to.

Are there any drawbacks? 

Predictive attribution, A/B testing, and lead scoring will help you identify your business’s most valuable prospects. But like anything else, there are always some drawbacks to consider before deciding whether it’s right for your business. 

Like any machine learning model, it takes time for predictive models to be trained. Currently, it takes around three months to train machine learning algorithms during a preliminary process.

But the upside is that once your model is set up, it scores visitors within two hours of their interaction. On the other hand, data-driven attribution models take seven days to appear within attribution reports in Google Analytics. 

So although there’s a longer preliminary setting up process for predictive scoring models, the shape scores visitors rapidly once it’s in place. 

You might also be concerned about the accuracy of machine learning. Can you trust these machine learning models to make accurate predictions about potential leads and purchasing behavior? 

Accurately predict customer purchasing behavior with Tomi.ai

There’s nothing wrong with using traditional A/B testing, lead scoring, and attribution methods. They help you understand your customers and make strategic data-driven decisions about your paid ad campaigns. But they’re not always the best choice for businesses with longer sales cycles or those with limited data — predictive models are. 

For example, real estate agencies, B2B SaaS, banking businesses, etc., where a customer’s LTV (lifetime value) typically matures, will benefit the most from predictive modeling. 

Using predictive machine learning models can get better insights into customer behavior and their likely purchasing habits. You’ll boost your ROI on marketing campaigns and help your sales team prioritize leads. 

The result? More conversions and less ad spend!

Ready to see how Tomi.ai can help you convert more prospects? Contact us today. 

Сообщение Predictive Lead Scoring, A/B Testing, and Post-Click Attribution появились сначала на Tomi.ai.

Ultimately, the most effective methods start with first-party data and predictive technologies. Predictive technologies analyze the behavior of visitors to your website (via organic and paid) and calculate the probability a user buys based on how they interact with your site—and how much they may purchase. 

All of this is first-party behavioral data that trains our machine learning algorithms. We don’t cross-pollinate the data sets from different customers. Once the data runs through Tomi.Ai’s algorithms, you can quickly gather very accurate predictions. Then, as more data comes in, you can continually iterate on your campaigns and prioritize conversions. 

There are specific resources out of the box with Facebook and Google’s ad networks that are doing a great job. And you can help them perform significantly better if you add predictive conversions, available immediately.

1. Conversion API

Before 2020, ad platforms didn’t factor any external sales data into their targeting. With the right developer resources in place, you can now set up the API and boost the ROI of your campaigns with this smart bidding technique.

The Conversions API is a Facebook API that can tie your offline conversions and website events data to Meta. Google also offers the AdWords API for offline conversions. 

Conversions API is a relatively new resource that Meta identifies can help you:

• improve your ad performance and attribution as customers interact with your brand on and offline
• optimize specific campaigns based on events that happen further down the funnel, such as post-subscription upsells
• reduce your cost per action as the API isn’t nearly as impacted by connectivity issues and ad blockers compared to pixel tracking 

Keep in mind if you’re optimizing your campaign for lead generation, you need to have enough conversions to optimize for sales: 50 per campaign.

Get faster conversion-driven data

While Conversion API has a lot of benefits, it may not be the best fit if you need conversion information sooner.

Working in any industry with a longer sales cycle can take you months to meet that 50 sales per campaign requirement. That sales timeline doesn’t always fit the ad network’s attribution window. Even when you have a real conversion you can share, Facebook has a 7-day attribution window and Google Network is 90 days.  

With predictive conversions, your feedback loop is exponentially faster. When you add a tracking pixel and use Tomi.ai to get predictive conversions, you have the predictive score ready to fire to an ad platform within two hours of a session. Any high intent visitor data goes straight to your ad platform so you can optimize ads quickly. You can make marketing decisions faster—including turning off ineffective channels or ads. 

2. Look-alike audiences

As you may know, targeting a lookalike audience tells your ad network you want people similar to ones that have already converted. Look-alike audiences take similar characteristics from your existing customers and geared with that data ad platforms attempt to target users with those attributes. 

For eCommerce brands, it’s easy to apply as you can create a look-alike audience based on people who purchased or have an item in their cart.  

In real estate and other industries with longer sales cycles, your options out of the box are more restricted. You can create an audience based on who visited your site, but that targeting is too broad. 

There’s also the option to add an audience similar to those that filled out your contact form. However, the targeting is still too broad and you may not have enough data to optimize properly. 

In theory, you can create an audience based on your customers that bought a house. Odds are you don’t have enough data to support that segment. This particular segmenting requires 1,000 entries. In other words, you need to have sold 1,000 houses already.  

Segment more effective audiences 

Predictive algorithms identify (or single out) your site visitors with the highest intent and create a lookalike audience based on more custom rules around more precise behavioral patterns. 

In an initial data-gathering phase, we analyze the behavior of buyers on your website to train the machine learning models. Next, any new users to your website receive a score based on their behavior. Our algorithms bucket users together that have the highest probability to buy. Then we push this audience data to ad platforms. Let’s say a conversion bought a home for $1 million. Predictive conversions can share with the ad networks to target more people like that conversion based solely on that user’s behaviors captured on your website.  

Machine learning analyzes thousands of patterns to build a more accurate prediction that targets leads in their customer journey when they’re actually ready to buy. We’ve found so far our models are 10x more accurate than a person can be with any behavioral segmentation.

3. Retargeting

Retargeting ads let a business reach users again to close more deals. 

Specific retargeting methods include:

Pixel-based retargeting – This is the most common method for retargeting where you tell the ad networks to retarget users based on specific events that occurred on your site such as visiting a specific page, filling out a form, or downloading an eBook.

Many brands start with this entry-level approach to retargeting. 

List-based retargeting – With list-based retargeting, you take someone’s contact information into your database and upload those email addresses to the ad network. The platform identifies users from those addresses and serves retargeting ads on their network.

Dynamic retargeting – This is traditionally an eCommerce approach to retargeting. Here, marketers create personalized ads based on a variety of factors such as:

• items left in a person’s cart
• geographic location
• previous purchases

Retargeting is an amazing tool. In eCommerce, it’s easy to tell ad platforms you want to target visitors who abandon their carts. 70% of carts end up abandoned, giving eCommerce marketers a low-hanging fruit that wins revenue back. In a long sales cycle, there’s nothing as obvious as that segmentation. Instead, you’re responsible for narrowing down the audience. 

But what if you could apply a more dynamic method to your non-eCommerce company and ensure ads reach your most high intent site visitors? 

Deploy a more effective approach to retargeting 

Rather than rely on widespread retargeting that diminishes your ROAS, you can draw clear conclusions about the intent of website visitors based on their on-site sessions and slice the retargeting segment further. Tomi.ai’s predictive scoring calculates every website visitor’s probability to buy. That way, you can retarget 3-5-10% with the highest potential to purchase—and not a broader bucket of users who show no sign they’ll ever buy from you.  

Predictive visitor scores take the concept of dynamic retargeting ten steps further. Tomi.ai can take the actions your site visitors complete in a session and continually re-score the likelihood that these users will buy from you. By doing this, you can segment your retargeting audience further based on the visitors who are the warmest leads.

Bring a more revenue-focused strategy to ad segmentation

B2B brands see substantial results applying machine learning models from Tomi.ai to their advertising. Our customers using predictive conversions see a 50-150% boost in their ROAS.

Find out more on how Tomi.ai can help you optimize every ad campaign. Contact us today.

Сообщение 3 ways to increase the effectiveness of your digital ad campaigns появились сначала на Tomi.ai.

These leads are the pillar of a successful sales funnel. Qualified leads provide a steady flow of potential customers and have a higher chance of converting into actual sales. They save time and resources by not having to chase after uninterested or unqualified prospects.

But here’s the truth: 26 percent of marketers consider understanding lead quality their biggest challenge. Finding quality leads can be a nerve-wracking and time-consuming process and requires research, patience, and a little bit of luck.

What is lead quality?

Lead quality refers to how likely a consumer is to convert into a paying customer. You measure it by factors such as their interest level, fit within your target market, and financial ability to purchase. The lead qualification process involves analyzing and evaluating these factors to determine whether a lead is worth pursuing. Most organizations have a process where the marketing department passes on leads to the sales team after vetting them for quality.

When a prospect engages with your company through a lead generation campaign, for example, they have the potential to convert if you nurture them correctly. These are marketing-qualified leads (MQLs). The marketing department then evaluates their level of interest and assesses their ability to buy to determine if they qualify as sales-qualified leads (SQLs). After this, the leads are passed on to the sales team, who close the deal.

Why is lead quality important?

There are several reasons why lead quality matters. It helps you:

Evaluate channel performance

Every marketing channel has its strengths and weaknesses, and the effectiveness of each channel varies depending on the target audience. A jewellery business, for example, may find that using paid search ads on Instagram has a higher conversion rate than the same ads on Facebook. Similarly, a B2B MarTech company may find that its target audience is more likely to convert with cold emails than direct mail.

If you understand which channels give you high quality leads, you can invest more in those channels and cut back on the ones that bring you a lot of leads but of poor quality. This will help you maximize your marketing budget and improve your ROI. It’s also crucial to differentiate between channels that generate demand and those that capture it. 

Some channels, such as paid search, may drive initial traffic to your website but don’t do much for turning site visitors into actual customers. Other channels, like email marketing, may not generate traffic but do a great job of converting leads into customers. A combination of both helps maximize your lead generation efforts.

Set realistic sales goals and forecast 

Sales goals and projections help you measure and plan for success, but they can only be accurate if you adjust the quality of leads. While bad leads do convert, they do so at a lower rate than high-quality leads. Enough high-quality leads ensure your sales goals and forecasts are accurate and realistic and lead to better planning and success in the long run.

Better lead quality lets you calculate how many potential customers you can realistically expect to close deals with and optimize your sales funnel accordingly. You can also use it to set expectations with your sales team, giving them enough time and resources to close deals without feeling pressured. This can help increase both their productivity and your company’s revenue.

Save time and resources

Quality leads are more likely to engage with your outreach and move through the sales funnel, resulting in a higher close rate and faster sales cycle. You can then prioritize these leads and spend less time chasing after unqualified prospects.

For example, if your target market is millennials in the healthcare industry, you may find that the best leads come from LinkedIn and Twitter. This means you can focus more on creating content and campaigns tailored to these channels, increasing your chances of converting high-quality leads. It also means that you can focus less on channels that are not as effective for your target market, saving time and resources.

How to set up a lead scoring system

16 percent of marketing professionals consider lead scoring the most essential strategic element of a successful lead nurturing program. Lead scoring helps prioritize leads by assigning them a numerical value based on their profile and behavior. Here’s how to set up a lead-scoring system:

Evaluate historical data

Dive into your contacts database and analyze the data you already have on hand. See who became customers in the past and learn what they had in common, such as industry, job title, and buying persona.

Also, look at the attributes of leads who didn’t convert and figure out what they had in common. Once you have a good sense of your target customers, you can set up a scoring system that prioritizes their needs and behaviors.

Identify a valuable lead indicator

Lead indicators are the actions and behaviors that signal a lead’s potential to become a customer. Events such as visiting your pricing page, downloading a whitepaper or making an inquiry through your contact form are all valuable indicators. Set up a rule-based pattern for each indicator and assign a numerical value. For example, a real estate marketing site may assign a value of 10 to leads who visited the mortgage calculator and area map, while a B2B MarTech company may assign a value of 20 to leads who signed up for their product demo.

With this information, you can create a complete customer journey map that shows all the different touchpoints and actions a lead may take before purchasing your product or service. Assign values to these touchpoints and collaborate with your sales team to determine how much these actions are worth in terms of closed deals and revenue. Once you have an idea of how many leads will convert into customers, you can set realistic sales goals for your marketing team and forecast more accurately.

Identify decision-makers

The B2B sales process is typically more complex than the B2C one, so it’s important to gauge which decision-makers in your target market have the power to make purchasing decisions. While it may be tempting to target CMOs and CEOs, decision-making power and authority often lie in other individuals.

The process typically involves six to ten people, so it’s important to identify the key players who can approve or veto a purchase. These may include sales engineers, research and development executives, product managers, and technical support staff. Include these individuals in your lead scoring system based on their influence and buying power in the organization.

Set thresholds

Achieving a score above a certain threshold could show that the lead is ready for sales outreach. Thresholds ensure that you’re not wasting marketing resources on unqualified leads and focusing your attention only on those who are most likely to become customers.

This may include assigning a score of 70 or 80 points in exchange for a demo request, or setting a “hot” threshold to indicate when the lead is ready for sales outreach. Define cold, warm, and hot lead thresholds as a guide for sales teams and adjust based on performance. For example, you may consider a lead above 50 warm, while a score above 75 may show a hot lead ready for immediate follow-up. Use these thresholds as a guideline for prioritizing leads, and adjust them over time as you gather more feedback from sales.

Sources to help you identify a “good” lead

Should you talk to your existing customers, see what your sales team wants, or analyze what the data is telling you? The answer is usually a combination of all three.

Sales team

Sales reps are in direct contact with customers and have a deep understanding of their needs. These reps know which marketing collateral, offers, or campaigns generate the most conversions and can provide valuable feedback on the quality of your leads. Find out what reps share with prospects in their initial conversations and interactions, and use this information to help you identify high-quality leads.

Customers

Get in touch with people who actually bought your product or service and ask them what convinced them to make the purchase. Sales reps and customers are two sides of the same coin. Understand how the sales process works for your customers.

Conduct customer interviews to understand how they felt about the sales process and what led them to buy from you. Include customers with long and short sales cycles to see what factors led to quicker or slower conversions.

Data

Analyze attribution data to see which content, such as ebooks or whitepapers, leads download before becoming customers. If historically, the data shows that buyers who download your ebooks are more likely to buy from you, then it’s safe to assume that the quality of leads is high.

Also, find out which activities have resulted in the most contacts. Certain lead gen forms, such as webinars or ebooks, may generate more leads at a lower cost than other forms. Assign scores based on the conversion rates for these activities to identify your most successful marketing efforts. 

Start generating better leads

Lead qualification is a comprehensive process that requires careful planning and implementation.

Every business should focus. on generating quality leads more likely to convert into customers – through targeted marketing campaigns, networking events, or lead scoring methods.

Advanced tools like Tomi.ai can use first-party behavioral data and machine learning to predict future sales and customer lifetime value. A focus on the best, most qualified leads will save time and resources for sales teams, leading to increased revenue and success for your business. 

Don’t let valuable leads slip through the cracks – start qualifying today!

Сообщение Lead Quality: What It Is and How You Can Improve It появились сначала на Tomi.ai.

You can definitely use this tool to test any standard hypothesis Bayesian equation. With this free calculator, you can test up to 10 variations at a time!

Here’s how to get the most out of this calculator

• To get proper results for variations such as users, sessions, or impressions we recommend breaking each variation into separate categories

• Once you separate the variation data, simply input the number of samples per variation, along with the number of conversions (such as clicks and goal completions).

• A good rule of thumb is to always double check your KPIs to get the most accurate and relevant results.

• Lastly — click “Calculate” — and you’re good to go!

Please Note

Our free A/B testing calculator gives you insights based off of binary models

→ To get the most out of your data, consider signing up for Tomi.ai to expand beyond binary models and into the realm of AI-powered predictive technology! Learn more here

Сообщение Tomi.ai’s A/B Testing Calculator появились сначала на Tomi.ai.

The problem with microsegmentation, however, is that by advertising only to the highest-converting sliver of your audience, you’re inadvertently ignoring the majority of your customer base.

Luckily, there’s a better way to hit your ROAS targets without sacrificing sales volume. Through newly available predictive optimization, you can still target your highest-performing audience segments without completely ignoring the rest.

Why advertisers love microsegmentation

In the world of online advertising, there are two traditional forms of audience segmentation:

#1 – Lookalike audiences – Targeting a lookalike audience means advertising to audience members that most closely resemble the people who have bought from you in the past. Ad networks compare demographics, interests, and behaviors to a list of customers you’ve provided.

For example, using Facebook lookalike audiences, you can upload a source audience of 100 to 5,000 customers. You can then use a percent range to tell Facebook how closely your new audience should match your source audience (e.g. target only the top 3%).

The Google Network has similar targeting settings, but calls these similar audiences and Customer Match.

#2 – Interest segments – The other segmentation option is to target audience members based on their personal attributes, interests, or demographics. This doesn’t require input about existing customers, but does require you to hand-select key audience

For example, Google audience targeting allows you to target audience members based:

• • Affinity: Users’ habits and interests

• Demographics: Age, gender, education, income, marital status

• Life events: Users who are about to graduate, get married, or have a baby
• In-market: Users who have signaled a purchase intent in your industry

If you were selling baby clothes, for example, you could target expectant mothers with disposable income. In both cases, you take 100% of the people available to you, and then you limit that audience to a microsegment based on data or your theory of your ideal customer. Advertisers love this approach because these segments convert at higher rates and therefore produce better ROAS.

The problem with microsegmentation

The problem with microsegmentation is that it’s binary. You either advertise to someone because they meet your segmentation rules, or you don’t. It’s all or nothing.

By condensing your audience to only your best-possible customers, you miss out on the majority of your customers and therefore miss out on the highest-possible sales volume.

Therefore, a lot of people who would have been interested in buying your product—had they heard about it—will never hear about your product because they didn’t make it through Facebook’s interest segment or lookalike audience algorithm.

To put it in numbers: The top percentile of your audience could have a conversion rate of 48%, but your 20th percentile may still convert at 9%. Is it worth ignoring that 20th percentile customer completely? Definitely not.

How many customers are you missing out on with microsegmentation?

Microtargeting causes you to lose out on 50 to 90% of your total possible customer base.

How did we arrive at that number? Let’s walk through an example using real Tomi.ai data from a client in the financial services space.

For the purpose of this example, let’s bin the customer’s entire possible audience into 100 buckets, each with 1% of their total audience population. The buckets could be binned by attributes such as predicted lifetime value of a contact.

If the customer advertised randomly to all 100 buckets, the conversion rates for each bucket would be distributed like this, with some buckets converting more than others:

Each 1% bin is sorted randomly along the x-axis. Conversion rate is on the y-axis.

In this example, the best bin converts at 48%, the second-best bin converts around 36%, and so on. There are plenty of bins at 0% conversion rate. The average is around 5%.

If we sort these bins by conversion rate, they would look like this:

1% audience bins sorted by predicted conversion rate (least-to-highest converting)

Then, you can look at each 1% bin in terms of what cumulative percentage of the orders they produced:

Looking at the cumulative order data, we see that the top 1% of the audience provide 8.7% of the customer’s true conversions. This means we condensed the audience into a bucket that buys 8.7 times more than the average audience member.

Going down the chart, we see the cumulative top 2% of the audience provide 15% of the orders. Advertising to this segment would net you 7.6 times the ROI than advertising to a random bin.

The top 10% of the audience provides 43% of the orders—4.3 times the advertising ROI.

If the client were using microsegmentation, they would use these top-performing audience buckets to target their ads.

For example, they could create a lookalike audience on Facebook using the top 2% of the audience, or they could use the top 10% of the audience to try to create audience segments with the right interest categories. For example, they could target customers buying insurance, people who have cars, fans of BMW, etc.

What’s the problem here?

By targeting the top 10% of your audience who account for 43% of your orders, you’ve left out 90% of your population who account for 57% of your remaining potential orders.

How does predictive optimization solve this problem?

To deal with this problem, Tomi.ai takes a creative approach to targeting and optimization.

At Tomi.ai, we use predictive optimization. This means advertising to your entire possible audience, but only bidding at the dollar amount that satisfies your ROAS requirement based on the conversion data you’ve collected about that audience segment.

In contrast, microtargeting is binary. You separate the good audience members from bad audience members. You do this because the lower audience segment is too diluted to be profitable when you look at the average.

But if instead of microtargeting, you optimize your bid to the level of dilution for that segment—i.e. bid less for lower-converting audience segments—you can still advertise to your entire audience with a profitable margin.

How Tomi.ai’s predictive optimization works

To illustrate how predictive targeting and optimization works, let’s return to our example financial services client above.

1. Website pixel – We start by installing a pixel on our client’s site to collect first-party visitor data. This allows us to track the on-site behavior of 100% of visitors over a period of time (e.g. one month).
2. Customer data – We then combine the website behavior with the client’s CRM sales data over that time period to get a complete picture of customer behavior.
3. Machine learning – Next, we use this customer data to train our machine learning models.
4. Optimization signal – The model then outputs the probability of an individual visitor’s likelihood to convert.
5. Smart bidding – Finally, the optimization signal data for 5 to 10% of highest engaged website visitors is fed into Google and Facebook’s smart bidding algorithm to bid the right dollar amounts for the right customers—without any targeting.
6. Profit! – The client sees high ROI without cutting their audience.

The key to success here is better input data provided by the website pixel and customer CRM. By using this data to feed your predictive model, you can equip the smart bidding algorithms with a frequent as well as nn accurate optimization signal instead of resorting to segmentation.

In summary, bidding through predictive optimization means taking the stance that, “every segment is good for me, as long as I advertise to that segment for the right price.”

Because microsegmentation only addresses about 10% of your customer base who provide 30-50% of your sales, you can double or triple your sales with a more profitable optimization approach.

Why aren’t people already using predictive optimization?

If predictive optimization results in more sales, why are so many advertisers still stuck on microsegmentation?

The answer is that there have been two major changes in the market:

1. Ad platforms are becoming smarter

One reason why bidding optimization has become a reality is because ad platforms like Facebook and Google have been shifting more and more to auto-bidding and smart-bidding strategies.

The better the input signal for these algorithms, the more they can optimize for the right audience.

For example, e-commerce businesses used to advertise to lookalike audiences. But because e-commerce businesses can train the smart-bidding algorithms with thousands of conversions, the smart-bidding algorithms have more than enough information to optimize for value-based signals like conversions or revenue rather than relying on segmentation (outside of new vs. returning customers).

This is a bigger challenge for businesses with long sales cycles and fewer conversion signals. This is one problem we’re trying to solve with Tomi.ai—by integrating customer behavior and CRM sales data.

2. Ad platforms are more open to conversion API integrations

The other shift has come from the ad platforms’ willingness to provide server-to-server integrations. These platforms used to only allow targeting through segmentation and lookalike audiences.

For example, Facebook only allowed server-to-server data integrations as of 2020, which was later called Conversion API. Previously, these server-to-server conversion integrations were only available to their top hundred or so advertisers.

This allows providers like us to integrate client data directly into their ad campaigns, creating smarter bidding strategies.

Optimize for the right signals with Tomi.ai

It makes sense why advertisers rely on segmentation. It’s a simple way to target high-converting audience members. That said, it has the unintended side effect of cutting out the majority of your audience.

Wide targeting and smart bidding optimization is the better approach—provided you have the right and frequent enough signal to optimize for.

By tracking behavioral customer data with Tomi.ai, you can provide the advertising algorithms with a non-binary input that allows the ad platforms to optimize for the right signal: the conversion likelihood value for the highest engaged potential customers.

The end result: more sales without sacrificing your ROAS.

To learn more about how Tomi.ai can win you more paying customers, contact us today.

Сообщение Why microsegmentation is holding marketers back появились сначала на Tomi.ai.

We’ve been there and know how to cure it.

With predictive targeting, you can supercharge your conversions by 2-4x and see a sales increase of up to 45%. Let’s jump into the details right away.

Predictive targeting: what it is and why it matters

Predictive targeting is a data-driven approach to acquiring customers. It uses deep learning algorithms, predictive analytics, and customer intelligence to find high-value prospects that are more likely to convert.

What makes predictive targeting stand out is its ability to pinpoint the right audience with a high degree of accuracy. This type of targeting involves data about a consumer’s past behavior, demographic characteristics, and other relevant information to identify patterns and make predictions about their likely future actions.

For instance, Tomi.ai uses first-party behavioral data from your website and sales data from your CRM to calculate the purchase probability for every new visitor and feed this data to Facebook or Google via APIs. 

Doing so helps ad platforms identify prospects that are more likely to buy right away and generate higher lifetime value.

Targeting is more than audiences.

3 pillars of effective targeting.

There’s a common misconception in performance marketing that the more segmented your audiences are, the higher chances you will generate more sales-qualified leads at a lower cost. In reality, though, who sees your message (ads) at what time of their buyer’s journey are more affected by a target event and a bidding strategy. 

In addition, we see more value in relying on broader audiences. Lastly, the ad message itself massively factors into engaging and converting advertising campaigns.

So babysitting your micro-segmented campaigns is not the best way to get the most out of Facebook and Google ads. What we suggest instead, let’s discuss in the next chapter.

4 elements of successful predictive targeting: audiences, bidding strategies, target events, and messaging

It’s important to understand the interdependent relationship between audiences, bidding strategies, and target events alongside the right messaging.

Audiences are the foundation of any targeting strategy. By breaking down the data you have at hand, you can create different audience segments that are more likely to convert. With a traditional approach, you would opt for narrow audiences, therefore, limiting Facebook or Google’s potential to find for you high-profitable customers within the broader audiences.

Bidding strategies allow you to allocate and optimize your budget in a way that gives you the most bang for your buck. Traditional performance marketing recommends bidding on CPA (cost per action) when predictive targeting favors a value-based approach, which helps build new larger high-performing lookalike audiences.

How to scale high-value customer acquisition with predictive targeting.

Target events are what you use to inform your bidding strategies. Traditional targeting implies target events like page visits, past purchases, sign-ups (for SaaS), or funded accounts (for finance). This approach might increase the number of leads but not necessarily translate into improved LTV since the sign-up event, for example, doesn’t guarantee conversions into paid customers. 

In contrast, you can leverage predictive targeting and optimize for strong, high-quality events like highly-active customers within a certain period of time or with recurrent payments, etc. 

Messaging should resonate with your target audience. It should be the one that not only generates clicks but drives high-profitable customers. How do you know which message will move the needle? Run a series of A/B tests to validate that message.

All that will help you maximize the impact of retargeting on marketing ROI and revenue. But how exactly? What to do next?

The optimization signal tradeoff and what to do about it

Optimization signals are the data points that are used to inform ad optimization decisions. The tradeoff is between relevance (targeting) and performance (bidding). 

For example, if you’re targeting a very narrow audience, you can be sure that the people in your target audience are more likely to convert. But if the target audience is too narrow, you might not be able to generate enough conversions to justify your ad spend. 

On the other hand, if you’re targeting a broader audience and using predictive analytics to optimize your bids, you might be able to generate more conversions. But the quality of those leads might not be as high as if you had been targeting a more specific audience. 

However, if you optimize for target events, you can ensure that your bids are optimized for the right audience while also making sure that you’re targeting leads that are more likely to convert.

Target events for value-based bidding.

Let’s see how to apply this knowledge from a real-life example. 

In the banking and insurance industries, there’s a clear trade-off — the stronger signal you have, like website visitors in terms of frequency, the less valuable this signal actually is. Will all those leads become funded accounts? Will they become high-LTV customers down the road? Likely not.

Here’s where predictive analytics comes into play. With that, you can build a probability distribution for your leads and website visits to understand, which visitor have a higher chance of converting into a high-value customer (e.g., the one who is approved for a credit card and then carries on the balance and pay you some interest). 

Such predictive scores you can then utilize for value-based bidding to increase the acquisition of high-quality leads.

How to act on predictive insights?

So what to do next to scale high-value customer acquisition once you have the scoring model and collected customer behavioral insights? Let’s focus on two key strategies.

6 strategies of how to employ predictive targeting.

Swap target events in prospecting campaigns.

Instead of optimizing for leads or funded accounts, or sign-ups, regardless of their value, you can now optimize for predictions of the likelihood of the visitor becoming an active customer. For the cash loan industry, you can even optimize for the number of commissions these people are going to pay you. 

Instantly, you have a tool to make Google and Facebook work harder to pinpoint the right audience for you.

Create larger high-performing lookalike audiences.

With predictive analytics, you can perform value-based segmentation to create larger lookalike and more precise audiences. As such, you can elicit people who are in the market right now seeking to get a new credit card and market your products to them with perfect timing.

Couple these two tactics and you’ll maximize your ROAS and ROI.

What results can you expect from predictive targeting?

We at Tomi.ai have successfully implemented the strategies discussed above for seven customers in the banking sector.

When we switch the target event from an actual funded account or a lead to a predictive event, we see about a 50% increase in approval rates on average. Next, the average increase in approved credit limit is about 25%, and the decrease in customer acquisition cost is 25-45%.

What’s more is that we managed to double the prospect-to-customer conversion rate. 

However, how does it work in practice?

How predictive targeting works — the tech behind it

We’ve touched upon the basic technical aspects above, but here we want to get into the nitty-gritty.

Let’s consider Tomi.ai. First, you need to install the pixel on your website so that the machine can start collecting the behavioral data of your visitors. Then, connect it to your CRM to gather information about 300 sales that are happening during the period since the pixel is installed. Next, Tomi’s predictive model bridges sales intent and site visitors’ behavioral data and analyzes multiple data points to find trends and patterns common to high-value customers. This process takes four to six weeks

The next step is to design a predictive score (predicted purchase value) for every new visitor. The training of the model takes 1-2 weeks, but once it’s up, the calculation of the score for every new visitor takes less than 2 hours.

As such, we have the right data to send to Google, LinkedIn, or Facebook as custom audiences so platforms can better train their models to identify the right buyer signals and show your ads to those people.

Delegate ads optimization to ad platforms

All this may seem like magic, but it is more than real. If you want to start working with predictive analytics, begin to trust machine learning to predict the future.

Want to learn more about predictive targeting? Book a personal demo with Tomi.ai and unlock your growth!

Сообщение How To Scale High-Value Customer Acquisition With Predictive Targeting появились сначала на Tomi.ai.

Therefore, even though your ROAS may seem good enough, you actually might be already losing money on your campaigns. Incrementality shows the efficiency of specific marketing interventions by identifying how much value each new dollar adds to the conversion outcomes and revenue. This is why measuring incremental, or marginal ROAS is key to analyzing your acquisition campaigns and drawing conclusions.

Today, we’ll talk about how you can use marginal ROAS to optimize your acquisition efforts and spending.

How to calculate marginal ROAS?

Measuring incremental values rather than absolute ones helps you exclude irrelevant conversions or spending data from your calculations. Likewise, measuring marginal ROAS disregards organic conversions or conversions driven by other channels or previous campaigns and considers changes in spending.

The basic calculation formula is the following:

However, there’s another way to calculate marginal ROAS using historical statistical data. Here’s how:

• Create a data set comprising daily historical spending from your ad platform vs revenue from CRM attributed to that spending;
• Clean and normalize the data using special algorithms;
• Visualize it on a line chart aligned by ascending spending and calculate n in the function y = x^n.

With this multiplier on hand, you can now calculate marginal ROAS:

Marginal ROAS = ROAS * n

In case you have little data or few resources to make calculations, you can use a simple rule of thumb. In our experience, for most campaigns, n lies between ⅔ and ½.

For example, if your baseline ad spend is $1,000, from which you earn $5,000 (i.e. ROAS = 5), each additional dollar spent will have a marginal ROAS of 5/1.5 = 3.3. In other words, this gives you a more realistic view of how successful your campaigns are.

What are the implications?

…for CMOs, Directors of marketing science, CEOs, and other C-suite executives

Calculating marginal ROAS helps prevent excess ad spending. When setting minimal ROAS for marketing campaigns, it’s worth taking incrementality into account.

For example, if you set minimal ROAS based on the break-even point, you might be losing money on certain ads or campaigns – because your marginal ROAS will be on average 1.5 times less.

One of the issues you might face if you have a long sales cycle or deferred revenues is that you might have difficulties calculating ROAS and marginal ROAS in real time. In this case, using predictive revenue calculation models might be helpful. For example, at Tomi.ai, we have developed such models to predict future sales and customer LTVs.

…for marketing managers

Bid caps and cost caps are two popular options used in Facebook ad campaigns. One of the bid strategy options is the minimal ROAS which you can use to optimize the campaign by targeting users who are most likely to convert into paying customers.

Indicating marginal ROAS instead of minimal ROAS in the bid cap strategy might be helpful for ad spend optimization. So, if you have a minimal ROAS of 3, for example, it’s recommended to multiply it by 1.5 to get the optimal result.

However, when optimizing ads by keywords, minimal ROAS coincides with marginal ROAS (i.e. the minimal profit you get for spending x dollars). In this case, average ROAS is only needed when calculating overall channel performance.

Final thoughts

Incrementality is something to be taken seriously when measuring your marketing performance and planning further investment. If not taken into account, you might end up with inadequate estimates and excess ad spending. To help you save more money, we at Tomi.ai have developed a predictive model for optimizing ad campaigns that considers both incremental costs and real-time user behavior.

Сообщение Why Marginal ROAS Matters and How to Measure It появились сначала на Tomi.ai.