Patent Application
20200074496
This invention relates generally to a message optimization system and, more particularly, to a system and method for recommending a grammar for a message campaign used by a message optimization system.
Commercial advertising has undergone a significant shift in the past decade. Traditional media advertising, taking the form of newspapers, magazines, television commercials, radio advertising, outdoor advertising, and direct mail, etc., has been decreasing as the primary method of reaching an audience, especially as related to certain target demographics or types of products. New media advertising, in the form of Popup, Flash, banner, Popunder, advergaming, email advertising, mobile advertising, etc., has been increasing in prominence.
One characteristic of new media advertising is the need to capture an audience's (viewers, readers, or listeners) attention with limited text. For example, with a banner or text message, the sponsor of the advertising message may only have a finite number of characters to persuade its audience to act by clicking on a link, texting back a message, etc. As a result, companies are increasingly interested in how to optimize their message, and the components in the message, to increase the message's response rate. International Publication Number WO 2011/076318 A1 discloses a system and method for optimizing a message and is incorporated by reference herein in its entirety. In this system, the message is divided into components and multiple values are tested for each component to determine the best response rates.
The best message optimization system, however, is only as good as its starting values. In the past, a user, or campaign manager, would manually choose the starting values. However, an inexperienced campaign manager may not know what values to start with. Therefore, there is a need for a system and method for recommending an initial grammar for a message campaign used by a message optimization system based on the data from previous campaigns.
The present invention is directed to a system and method for recommending a grammar for a message campaign used by a message optimization system. The method includes enabling a campaign manager to specify one or more parameters for a new campaign. A set of statistical design budgets is calculated based on one or more of the specified parameters. In one embodiment, the statistical design budgets are calculated based on audience size, effect size, and expected response rate.
At least one grammar structure is recommended based on the set of statistical design budgets, where the grammar structure specifies a plurality of message component types. A campaign manager is then able to select one of the recommended grammar structures for the new campaign. The n-most relevant past campaigns are identified based on the specified parameters. For each message component type in the selected grammar structure, a ranked list of previously used values for the component type in the n-most relevant past campaigns is generated, where the values are ranked at least in part based on performance in the n-most relevant past campaigns. For each message component type, a plurality of values is selected to recommend based at least in part on the ranked list.
The campaign manager is able to reject one or more of the recommended values. In response to the campaign manager rejecting one or more of the recommended values, alternate recommended values are provided for the rejected values. In certain embodiments, providing alternate recommended values for the rejected values comprises choosing values from the next best performing tag or another candidate value associated with the same tag as the previously rejected value. In one embodiment, the method further comprises evaluating the recommended grammar based on various computed metrics.
In one embodiment, the previously used values are each associated with a semantic tag. Generating the ranked list includes identifying the tags associated with the previously used values in the n-most relevant campaigns. The tags are then ranked based on performance of tagged values in the n-most relevant campaigns. The previously used values are ordered first by ranked tag group and second, within each tag group, by the number of time an individual value has been identified as the winning value in the n-most relevant campaigns.
In one embodiment, the recommend values are selected in part from the ranked list of previously used values and in part from a ranked list of untested values. A plurality of untested values may be grouped by semantic tag and ranked according to their semantic tag, where within a tag group an untested value is randomly ranked. The percentage of recommended untested values may depend on a degree of exploration/conservatism indicated by the campaign manager.
In one embodiment, enabling a campaign manager to specify parameters for a new campaign includes providing a user interface wherein the campaign manager is prompted to enter parameters for the campaign. One or more parameters in the user interface may be associated with a drop down menu that lists the options available to the campaign manager for the parameter. The parameters may comprise campaign duration, audience size and characteristics, expected response rate, effect size, constraints, and objectives of the campaign. The campaign manager may select the objectives of the campaign via a drop down menu, from a sliding scale, or by inputting a value. In certain embodiments, if the campaign manager does not specify certain parameters, default values, empirically determined based on past campaigns, are used.
The present invention provides many unique advantages. For example, multiple campaign managers are able to centrally and efficiently access the consolidated, distilled information from each of their individual campaigns. This allows novice campaign managers to become productive very quickly and allows individual campaign managers to manage a greater number of campaigns at the same time. For example, whereas previously a campaign manager would need at least 2 to 3 hours to run a single campaign, a campaign manager using the present system is able to process campaigns orders of magnitude faster.
The present invention provides a system and method for recommending a grammar for a message campaign used by a message optimization system. The message optimization system uses the grammar to automatically generate variations of the message and test at least some of the variations.
As used herein, a “component” is a functional part of a message. Components may be tangible, such as an introduction, benefit, or a call to action, or they may be intangible, such as the level of formality, the verb tense, or the type of persuasion. Components are filled with “values,” which may be categorized by families. For example, values filling the introduction component may be from the greetings category (e.g., “Congratulations!”, “Hello!”, etc.) or they may be from the urgency category (e.g., “Only Today!”, “Act Now!”, etc.) or from other categories. The type of components and the values tested for each component are the “grammar” of the message. The invention as described herein applies to context-free grammars and grammars that are not context-free.
Shown below is a simple example grammar named “sentence”, consisting of four components: “intro”, “benefit”, “product”, and call to action or “cta”. Next to each listed component are values that may fill the component. For example, the introduction component may be filled with “Congratulations!” or “Great news!”.
A “campaign” is the process for testing grammar structures and values for the structures to determine which structures and values receive the best response rate, where the “response rate” is the acceptance of a call to action (e.g., calling a number, sending an sms to a number, clicking on a weblink, clicking on a mobile weblink, proceeding to purchase, etc.). The “expected response rate” is the rate the campaign manager expects to get from the target audience for the campaign (e.g., 3%, 5%, etc.). The “effect size” is the percentage difference in the response rate that can be created by varying the values of the components. A “campaign manager” is the user of the message optimization system.
The system calculates a set of “statistical design budgets” based on audience size, effect size, and expected response rate (step 115). The statistical design budget for a campaign is the number of components in a message and the number of values to test for each component. Different variations can have the same or almost the same statistical design budget. For example, a grammar with three message components and four values for each component will generate the same number of message variations as message with four components and three values to test for each. The number of values associated with each component can vary within a grammar. For example, there may be five values for one component and two values for another.
If the effect size is small (i.e., the percentage difference between the response rate of two values is small), then the audience size must be large in order to return statistically significant data. For example, if Component Value A has a response rate of 3.0% and Component Value B has a response rate of 3.3%, the difference in absolute terms is 0.3% or an effect size of 10%.
A person skilled in the art would understand that an effect size of 0-5% is negligible, such that it would be very hard to differentiate between variations in response rates and inherent noise. An effect size of 5-10% is generally considered very small and would require a very large sample in order to establish the statistical significance. An effect size of 10-15% is considered small and would require a relatively large sample, but not as large as the sample required for an effect size of 5-10%. An effect size of 15-30% is generally considered “medium” and is the focal point of interest in most optimization scenarios. An effect size of 30% or greater is large and usually observed when using extreme component values or with very small response rates. In the above example, the system would need a very larger audience size in order to produce useful data.
The expected response rate is the response rate (from the target audience) that the campaign manager expects to get for the campaign (e.g. 3%, 5%, etc.). In one embodiment, a campaign manager can specify the effect size and response rate in step 110. If the campaign manager does not specify the effect size and the response rate, the system may use default values that are empirically determined based on past campaigns.
Returning again to
If the campaign manager accepts one of the recommended grammar structures (step 125), then, for each component type in the grammar structure, the system generates a ranked list of previously used values in the n-most relevant past campaigns (step 130). In the preferred embodiment, values are ranked by semantic meaning (i.e., tag applied by the campaign manager) and, within a semantic grouping, by performance (i.e., response rate). The n-most relevant campaigns are determined using the parameters entered by the campaign manager in step 110.
For each component in the selected grammar structure, the system selects a plurality of values to recommend based at least in part on the ranked list generated for that component type (step 135). Other factors that the system may consider in recommending candidate values are values that satisfy any constraints on category or length (i.e., values that do not satisfy length/category strength are filtered out), winners from grammars in similar past campaigns, values that have performed well in general, or values from lists of untested values. The system may also consider synonyms or semantically related values to other well performing candidate values from past campaigns. Values across languages may be chosen based on their translation. The system may also select values from both a ranked list of previously used values and in part from a ranked list of untested values based on the degree of conservatism/exploration indicated by the campaign manager. The number of values recommended for each component is based on the parameters of the statistical design budget for the selected grammar. The recommended grammar is then presented to the campaign manager (step 140).
In one embodiment, the system computes various metrics for the recommended grammar to enable the campaign manager to evaluate the recommended grammar (step 145). Factors affecting the evaluation of the recommended grammar may include the degree of exploration of the grammar (i.e., how many new/untested component values are being tried versus how many are tried and tested), the “structural spread” of the grammar (i.e., whether the grammar branches out across a wide variety of syntactic constructs or whether it narrowly explores a small area), how much of the design budget is being spent (i.e., how many messages does the experimental design predict we need to send in order to fully test the grammar), how is the distribution across message components (i.e., are some components being tested across a wide range of candidate values and others only a few), amount of wasted text space (i.e., whether short components are being compared with long components where the long component values are reserving unnecessary space), how close we are to the optimal grammar (i.e., sometimes the campaign manager will choose a suboptimal candidate value as the top values from the top performing tags may be overused), degree of repeatability (i.e., how similar is the recommended grammar to the grammars that have recently been used on this audience), and degree of novel investment (i.e., whether the system is testing a large number of values in components that generally have fewer combinations or vice versa). The metrics applied to the recommended grammars need to be interpreted in light of the parameters of a particular campaign and the objectives of the campaign manager.
The campaign manager is able to reject recommended component values (step 150). For example, there may be legal or branding issues with certain expressions that the campaign manager wishes to avoid. If the campaign manager rejects one or more of the component values (step 155), the system provides alternate values (step 160). For values chosen from a ranked list, the system selects the next best value(s) that satisfies any specified constraints (e.g., semantic tag constraints, size constraints, etc.). The campaign manager may choose values from the next best performing tag or another candidate value associated with the same tag as the previously rejected value. The system then computes the various metrics for the recommended grammar with the alternate values according to step 145. If, however, the campaign manager does not reject any component values, the system “commits” the grammar for processing (for use in the campaign) (step 165).
In certain embodiments, data from past campaigns is stored in one or more databases. The data may include the grammar that was used, parameters related to the campaign (e.g., country, client, language, campaign duration, product, type of offer, promotion channel, activation/participation channel, whether the campaign is a continuation or repetition of a previous campaign, audience size and characteristics, expected response rate, effect size, constraints, grammar quality, objectives of the campaign, etc.), the response rate, the corresponding pValue for the response rate, etc. The response rates, pValues, and other data may be automatically entered by a message optimization system when a campaign is run. Typically, a campaign manager manually tags values with semantic tags.
In one embodiment, the tags and component values are ranked as follows:
For every cell value m(i, j), where 0<i≤t, 0<j≤t, and t is the number of tags in the system, information about Tag i competing against Tag j is computed as follows:
1. Compute the number of times a component text that is tagged with Tag i is chosen over a component text that is tagged with Tag j; compute the number of times a component text that is tagged with Tag j is chosen over a component text that is tagged with Tag i; compute the number of times the results show that the performance of Tag i and Tag j are not statistically different.
For every cell value m(i, Total), a summary of the performance of Tag i across all tags is computed by aggregating all the cell scores in the row for that tag. The number may be zero as the two tags may have never competed against each other.
The above matrix may be computed each time the system recommends a new grammar because the campaign manager may give different parameters and the system may select a different set of n-most relevant campaigns.
The system ranks the tags from the highest Total to the lowest Total score. For each component type, a list is then created of all the component values grouped by tag and ordered first by tag and then within each tag by the number of times the individual component value has been identified as a winning value in any past grammar in the set of n-most relevant campaigns. From this list, the component values are selected to fill the grammar structure based on the design budget (e.g., if the design budget calls for four introduction component values, the system selects the first component value that comes from the top four component tag groups, filtering out inappropriate values based on constraints).
In one embodiment, if two tag groups are equal in performance (or are untested relative to each other), the ranking between the two tag groups is randomly determined. In another embodiment, the ranking is based on a predefined default. In still another embodiment, for breaking ties between tag groups, the system will consider a global ranked list that takes into account ranking information of all campaigns and not just the n-most relevant campaigns.
Depending on the ratio of conservatism to exploration indicated by the campaign manager, the system may add some untested values into the campaign. The more conservative the campaign, the more values are selected from the ranked list of tested values from previous, relevant campaigns, but the more exploratory the campaign, the fewer tested values are selected and the more untested, or little-tested values, are selected.
From the lists generated according to the methods of
Client Applications 505 provide a user interface via which users (e.g., campaign managers) can enter parameters for a new campaign and review and/or modify a recommended grammar. The Client Applications 505 may be run on any number of systems and may connect to the Recommendation System 500 through any number of channels. For example, the Client Applications 505 may include web, desktop, or mobile applications.
The Recommendation System 500 has a User Interface (UI) Generation Module 510, a Recommendation Engine 520, a Ranking Module 530, a Relevant Campaign Retrieval Module 540, a Statistical Design Module 550, a Tagging Module 560, a Grammar Evaluation Module 570, and a Database Interface 580.
The UI Generation Module 510 provides a user interface between the client applications 505 and the Recommendation Engine 520. The Recommendation Engine 520 recommends an initial grammar for the message campaign used by the message optimization system. The Ranking Module 530 ranks a plurality of semantic tags based on the past performance of the previously used values associated with each semantic tag. The Ranking Module 530 then orders the previously used values first by ranked tag group and then within each tag group. The plurality of untested values is grouped by semantic tag, but then randomly ordered within the tag group.
The Relevant Campaign Retrieval Module 540 retrieves the relevant past campaigns given the set of user specified input parameters. The Statistical Design Module 550 produces a set of statistical design budget options. The Tagging Module 560 interacts with the UI Generation Module 510 to allow the campaign manager to tag the components with semantic tags. The Grammar Evaluation Module 570 computes metrics for a recommended grammar given the set of input parameters. The Database Interface 580 interfaces with one or more databases 590, which functions to store past campaign data (e.g., the grammar for each campaign, the response rate for each tested value, the pValue indicating the statistical significance of the difference between tested values for a component, etc.).
The methods described with respect to
As will be understood by those familiar with the art, the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Accordingly, the above disclosure of the present invention is intended to be illustrative and not limiting of the invention.
This application is a continuation of U.S. patent application Ser. No. 13/474,695 titled “System and Method for Recommending a Grammar for a Message Campaign Used by a Message Optimization System,” which was filed on May 17, 2012, the contents of which are incorporated by reference as if fully disclosed herein.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13474695 | May 2012 | US |
| Child | 16508600 | US |
