The minimum number of records required to build a robust look-alike model is 300. We ask for your customer data input to be larger — 750 records — because not all of the records may be eligible for building the look-alike model:
After uploading your customer data file, the file goes through data manipulation, standardizing, and cleansing processes. There will likely
Your customer input file is used to create the look-alike model and to build a completely separate Validation sample, on which we apply and validate the newly built look-alike model. Validation of the new model with a separate sample confirms the precision of our model and is a standard procedure when we build the models but require more records.
Yes. You can easily take predictive (or DDG’s) lists and set up custom audiences for targeting on Google AdWords and Facebook Advertising. Export your list from the Reach Predictive Platform and then follow the directions below.
Inputting Your List Into Google AdWords Login to your company’s AdWords account. In the upper right corner of your screen, find ‘Tools, Billing and Settings’ (a wrench icon) and click to open. Select ‘Audience Manager’ under ‘Shared Library’. Click on ‘Audience Lists’ from the menu on the left and then click the plus button to create a new audience list.
In order to upload your
From here, you can set a membership duration (the default is unlimited) and click ‘Upload and Create List.’ Your data file can take up to 48 hours to upload completely and you can watch the progress of the upload under “Audience Lists.”
Inputting Your List Into Facebook Advertising Login to your company’s Facebook Ads Manager account. From the left-most
Facebook will show you a preview of your data and how they would classify it. Make sure that they have given your data the greenlight. If your data has been flagged in orange, just make sure that Facebook is mapping the right field for that value. If you see a lot of errors, it may be because Facebook is looking for the wrong delimiter (the punctuation mark that separates data points). You can change this by hovering over “modify the delimiter” and choosing a new one.
Facebook then hashes your data, uploads it and creates your audience for you. The upload time varies depending on how large your file is. Feel free to continue working in another browser tab or window while you wait. When Facebook is done creating your Custom Audience, they automatically generate a list of next steps you can take to get the most out of it. You can take one of those next steps immediately or click ‘Done’ to finish.
Our solution is not optimized to maximize the percentage of your data that you upload.
At this stage,
Look-alike models analyze the profile of your customers using predictive analytics and machine learning techniques to identify what is unique about your customers and what characteristics set them apart from the rest of the population. Once you have built this model, you are in a position to identify, within the broader population, individuals who match the profile of your customer. Three key steps must take place to build a look-alike model:
Match your clean and de-duped customer data with data from
Append a list of potential customers from the same geographical area, for which we also have the same variables available
Apply predictive analytic techniques to identify which combination of variables enables the best differentiation between customers from non-customers
Look-alike models allow you to identify prospects with the right profile to be your customers in a
Yes. If you have customers, we can help you identify potential individuals who look just like them. Our solution can be applied to a wide variety of customer types, regardless of the industry. The math behind our solution uses demographic measurements (e.g. age, marital status), purchasing behavior (e.g. online/offline orders), and many other characteristics that every customer in every industry has – rest assured we can find people who look like your existing customers.
A response model identifies people likely to reply to a specific marketing campaign. Typically, response models are more focused than look-alike models in that they produce a higher response rate – the response model is built using people who are already responding to one given campaign. Building a response model requires the following steps:
Execute an initial marketing campaign either on a random population or on a portion of the population selected through a lookalike model. The targeted campaign population should be large enough to ensure that you receive a few hundred responders.
Campaign responses can then be analyzed through a predictive model after appending 3rd party data to the entire campaign population. The response model identifies what differentiates responders from non-responders.
The response model can then be applied to identify people within the population most likely to answer a specific marketing campaign.
The look-alike analysis does not guarantee any response rate. Response models (link to Response model) predict response rates. Once you have downloaded a list of prospects, you can then try different marketing strategies in order to identify what type of marketing mix is the most efficient to convert these prospects into customers. The next step would be to provide a list of all the people you contacted with a specific marketing campaign and run a response analysis – this analysis will enable you to identify which people are likely to respond positively to your marketing message.
When using predictive marketing, it is important to ensure that the information which is analyzed is relevant. Indeed, no matter how sophisticated the machine learning algorithms are, they can only find patterns that are not completely buried under noise. This is why
All 3rd party data used in the predictive models have been vetted and analyzed by our data experts as well as by our data scientists to ensure that the data are of the highest quality. (link to Where is the potential customer data coming from?)
We apply the same vetting
Once the first 2 steps are in place, our predictive engines build 100 predictive models on different splits (sample) to ensure that identified patterns are reproducible and not simply due to specific sampling strategies that we use. In other words, we ensure that the models built by
Yes, we have multiple filters that can be applied to further assist in selecting your new leads. Without any filters applied, your best prospects will be selected from the same geography where your current customers,
You will be able to choose from:
your customers’ existing geography (based on the uploaded customer file)
entire US geography
customized list of states and/or zips (state or zip code lists can be uploaded and the geography can be expanded upon by increasing the radius of the area)
You will be able to set up filters by:
Marital Status, Presence of Children, Home Ownership, and ranges of Household Income
One additional filter that can be applied is through the use of suppression files. You have the ability to upload a suppression file before purchasing your new leads.
Once the look-alike model is built, you have the option of discovering new leads across the entire country, even if your market is regional. Reach Analytics can identify new targeted leads with similar characteristics that may be found outside of your current market footprint. See question ‘Is it possible to add filters on my targeted leads?’ for more information on defining specific geography.
The index is actually a relative measurement. An index enables you to easily understand if a variable’s value is above or below the population average. For instance, let’s imagine that you only have customers in Texas. What we do is calculate the average of each variable for the Texas population and assign it an index of 100. As an example, if the average income in Texas is $50,000 and your customers have an average income of $75,000, then the index would be 150 (calculated as 75,000/50,000*100). The index
While you may have customers in certain geographies, chances are that people in other areas of the country have the right profile to be interested in your offering. While
Univariate non-linear models: the first step consists of encoding the variables so that we catch the
Monte Carlo: Reach uses Monte Carlo techniques to orchestrate the creation of 100's of models which are built in parallel on different subsets of the data.
Regression: Polynomial models are built on encoded data using different subsets of the training population in order to substantiate that the analysis
You will not necessarily produce the exact same results; you might have some small differences. Before building our predictive models we prepare the data and create a market base which enables the identification of variables that distinguish your customers from the general population. On top of the list of customers you provide us, we add a random list of non-customers (a few hundred thousand).
This list of non-customers is actually chosen randomly with only one constraint, they need to come from the same geography where you have customers. The random selection of these non-customers ensures the results are not biased in any way. Two analyses would be run on two slightly different datasets ultimately producing models that will not be exactly the same.
The fit measure is a calculation of the accuracy of the model. This measure allows you to gain an understanding of how good a predictive model is.
A score of 0 means that the analysis was not able to find factors which differentiate your customers from the rest of the US population. Essentially a fit of 0 means that selecting your population randomly is as good as what the predictive model is able to do.
On the contrary, a fit of 100 means that the model is perfectly able to separate customers from non-customers. It is the perfect solution.
Predictive analyses with accuracy over 95% are very suspicious and we recommend
The lift chart, displayed on the analysis tab, is a traditional measure used in the marketing world in order to identify whether the predictive analysis made is relevant. After building
Key predictors or predictive variables correspond to the variables which set apart your customers from the rest of the population. These are the variables that were retained by our self-learning predictive analytics engine. The machine learning algorithm tests over 600 variables and 100 predictive models. By combining these models, we are then in a position to identify the variables that are differentiating between customers and potential customers in the most efficient way.
Please note that for each option we provide various volume discounts and would be happy to answer any questions. Please contact us at email@example.com.
Identifying, at the
A data hygiene technique for eliminating copies of repeating data records.
If a consumer file (without duplications) is loaded, we typically match 40 - 75% of the input file. A low percentage means that the file has low integrity, meaning:
A lot of duplicates
Bad or incorrect addresses
Several family members in the same file (householding would reduce volume)
Use of PO Box or business address instead of physical home address
Providing a high match rate is relatively easy, all one
Records with inaccurate, outdated, or false data. Most often, marketers encounter ghost data in the form of outdated addresses, which can lead to poorly personalized and/or targeted messaging.
The National Change of Address is a solution offered through the United States Postal Service (USPS) which makes
Simply put, poor targeting kills your ROI. Whereas NCOA depends on consumers actively filing their updated address with the United States Postal Service, Integrity goes above and beyond by identifying and suppressing ghost records and fill-in data from mailing lists. Best practice consists
Upload your database into our cloud platform (with name and address fields). That’s it. Our smart matching algorithm evaluates the input data against our pre-cleansed, proprietary database made up of billions of multi-sourced, third-party data points. Once your check is complete, you can export cleaned, household data back into your CRM or marketing engine of choice.
Our data is regularly updated across a span of internal and third-party sources and regularly checked for integrity.
Save thousands of dollars in marketing costs per campaign.
Lift overall response rates.
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