Segmenting sounds activities
Blending and Segmenting Games | Classroom Strategies
Children who can segment and blend sounds easily are able to use this knowledge when reading and spelling. Segmenting and blending individual sounds can be difficult at the beginning. Our recommendation is to begin with segmenting and blending syllables. Once familiar with that, students will be prepared for instruction and practice with individual sounds.
| How to use: | Individually | With small groups | Whole class setting |
What are blending and segmenting?
Blending (putting sounds together) and segmenting (pulling sounds apart) are skills that are necessary for learning to read and spell. When students understand that spoken words can be broken up into individual sounds (phonemes) and that letters can be used to represent those sounds, they have the insight necessary to read and write in an alphabetic language. Blending and segmenting games and activities can help students to develop phonemic awareness, a strong predictor of reading achievement.
Why teach blending and segmenting?
- Teaching students to identify and manipulate the sounds in words (phonemic awareness) helps build the foundation for phonics instruction.
- Blending and segmenting activities and games can help students to develop phonological and phonemic awareness.
- Developing phonemic awareness is especially important for students identified as being at risk for reading difficulty.
How to teach blending and segmenting
Segmenting and blending — especially segmenting and blending phonemes (the individual sounds within words) — can be difficult at first because spoken language comes out in a continuous stream, not in a series of discrete bits. Beginning with larger units of speech can help.
Early in phonological awareness instruction, teach children to segment sentences into individual words. Identify familiar short poems such as "I scream you scream we all scream for ice cream!" Have children clap their hands with each word.
As children advance in their ability to manipulate oral language, teach them to segment words into syllables. For example, have children segment their names into syllables: e.g., Ra-chel, Al-ex-an-der, and Rod-ney. Likewise, have them blend syllables to make words.
Once in kindergarten, the focus of blending and segmenting instruction should shift to the phoneme level. This work can be challenging for students, so it can be useful to know which scaffolds can help students make the leap.
- Start with words that have only two phonemes (for example, am, no, in)
- Begin with continuous sounds (phonemes that can be held for a beat or two without distorting the sound). Have students practice blending and segmenting words with continuous sounds by holding the sounds using a method called “continuous blending” or “continuous phonation.” (e.g., “aaaammmm ... am”)
- Then, introduce a few stop sounds (phonemes that cannot be held continuously).
Ensure that students articulate the sounds cleanly, without adding an “uh” to the ends of sounds such as /t/ and /b/. Stop sound at the end of words (eg. at, up) are easier to blend than those that have stop sounds at the beginning (for example, be, go) - As students are ready, progress to words with three phonemes, keeping in mind that words beginning with continuous phonemes (for example, sun) are easier to blend and segment than those with stop sounds (for example, top).
- As students become more skilled at blending and segmenting, they may no longer need to hold sounds continuously, transitioning from “ssssuuunnn” to sun.
It can be helpful to anchor the sounds students are working with to visual scaffolds. Elkonin boxes, manipulatives (such as coins or tiles), and hand motions are popular supports. It’s important to remember, however, that the goal of blending and segmenting games is literacy and there is no better visual representation for a phoneme than a letter.
Collect resources
Blending: guess-the-word game
This activity, from our article Phonological Awareness: Instructional and Assessment Guidelines, is an example of how to teach students to blend and identify a word that is stretched out into its basic sound elements.
Objective: Students will be able to blend and identify a word that is stretched out into its component sounds.
Materials needed: Picture cards of objects that students are likely to recognize such as: sun, bell, fan, flag, snake, tree, book, cup, clock, plane
Activity: Place a small number of picture cards in front of children. Tell them you are going to say a word using "Snail Talk" a slow way of saying words (e.g., /fffffllllaaaag/). They have to look at the pictures and guess the word you are saying. It is important to have the children guess the answer in their head so that everyone gets an opportunity to try it. Alternate between having one child identify the word and having all children say the word aloud in chorus to keep children engaged.
Talking in "Robot Talk," students hear segmented sounds and put them together (blend them) into words. See robot talk activity ›
See all Blending/Segmenting Activities from the University of Virginia PALS program
Blending slide
The "Reading Genie" offers teachers a simple way to teach students about blends. Teachers can use a picture or small replica of a playground slide and have the sounds "slide" together to form a word. See blending slide activity ›
Oral blending activity
The information here describes the importance of teaching blending skills to young children. This link provides suggestions for oral sound blending activities to help students practice and develop smooth blending skills. See oral blending activities ›
Sound blending using songs
This activity (see Yopp, M., 1992) is to the tune of "If You're Happy and You Know It, Clap Your Hands."
If you think you know this word, shout it out!
If you think you know this word, shout it out!
If you think you know this word,
Then tell me what you've heard,
If you think you know this word, shout it out!
After singing, the teacher says a segmented word such as /k/ /a/ /t/ and students provide the blended word "cat.
"
Segmenting cheer activity
This link provides teachers with information on how to conduct the following segmentation cheer activity. See segmenting cheer activity ›
Write the "Segmentation Cheer" on chart paper, and teach it to children. Each time you say the cheer, change the words in the third line. Have children segment the word sound by sound. Begin with words that have three phonemes, such as ten, rat, cat, dog, soap, read, and fish.
Segmentation Cheer
Listen to my cheer.
Then shout the sounds you hear.
Sun! Sun! Sun!
Let's take apart the word sun.
Give me the beginning sound. (Children respond with /s/.)
Give me the middle sound. (Children respond with /u/.)
Give me the ending sound. (Children respond with /n/.)
That's right!
/s/ /u/ /n/-Sun! Sun! Sun!
Segmenting with puppets
Teachers can use the activity found on this website to help teach students about segmenting sounds.
The activity includes the use of a puppet and downloadable picture cards. See segmenting with puppets activity ›
Differentiate instruction
For English-learners, readers of different ability levels, or students needing extra support:
- Incorporate print into blending and segmenting the individual sounds in words with students who know the spelling-sound correspondences in the words.
- Use picture-centered activities to support English-learners and younger students.
Related strategies
Find more activities for building phonological and phonemic awareness in our Reading 101 Guide for Parents.
See the research that supports this strategy
Chard, D., & Dickson, S. (1999). Phonological Awareness: Instructional and Assessment Guidelines.
Clemens, N., Solari, E., Kearns, D. M., Fien, H., Nelson, N. J., Stelega, M., Burns, M., St. Martin, K. & Hoeft, F. (2021, December 14). They Say You Can Do Phonemic Awareness Instruction “In the Dark”, But Should You? A Critical Evaluation of the Trend Toward Advanced Phonemic Awareness Training.
Fox, B., & Routh, D.K. (1976). Phonemic analysis and synthesis as word-attack skills. Journal of Educational Psychology, 68, 70-74.
Gonzalez-Frey, S. & Ehri, L.C. (2021) Connected Phonation Is More Effective than Segmented Phonation for Teaching Beginning Readers to Decode Unfamiliar Words. Scientific Studies of Reading, 25:3, 272-285.
Sensenbaugh. (1996). ABCs of Phonemic Awareness.
Smith, S.B., Simmons, D.C., & Kameenui, E.J. (February, 1995). Synthesis of research on phonological awareness: Principles and implications for reading acquisition. (Technical Report no. 21, National Center to Improve the Tools of Education). Eugene: University of Oregon.
Yopp, H. K. (1992). Developing phonemic awareness in young children. The Reading Teacher, 45 , 696-703.
5 Activities for Developing Phoneme Segmentation Skills
Sharing is caring!
34 shares
- Share
- Tweet
Phoneme segmentation is a foundational skill for reading and writing.
It’s important to start teaching phoneme segmentation skills early so children can develop this skill before it becomes more difficult to learn.
Mastering phoneme segmentation helps readers break apart sounds in a word, blend them together, and read! Here are five activities you can do at home or school to teach this skill.
What is phoneme segmentation?
Phoneme segmentation is the ability to break a word up into its individual sounds. It is on the development continuum of Phonemic Awareness, along with rhyming and blending, but is one of the more difficult concepts for children to grasp.
Phonemic awareness refers to the ability to hear the sounds of language. It includes learning how to phonetically segment (break apart) a word into individual sounds and blend different combinations of sounds together to form words.
Reading is made possible by the ability to hear each sound within spoken words. When children can’t accurately detect or distinguish between these sounds, they struggle to decode and understand what is being read.
Phonemic awareness is an early step in learning to read, and is a key component of decoding.
Phonemic awareness helps students learn the relationships between letters, letter patterns, and sounds; it also helps with spelling and writing.
When you segment a word, you break it down into its individual sounds.
For example, let’s look at the word cat. We can segment this word into 3 sounds: /c/ /a/ /t/
Let’s try the word purple. We can segment this word into 4 sounds: /p/ /ur/ /p/ /l/
Why is phoneme segmentation important?
Segmenting words into individual sound units enables students to focus on the basic elements that make up spoken language. Students must be able to decode words into their basic phonemic chunks before they will be able to read fluently and with comprehension.
Children who are better at recognizing “sounding out” words have stronger abilities in literacy acquisition, vocabulary development, spelling, and knowledge of how letters represent sounds.
Phoneme segmentation is an important early step in learning how to read, so let’s get started!
segmenting the word bat with magnetsWhen to teach phoneme segmentation
Students should already be familiar with segmenting and blending compound words and syllables (all included in my Phonemic Awareness Task Cards). Once they can confidently segment and blend compound words and syllables you can move on to working with phonemes.
How to teach phoneme segmentation
You should start by using manipulatives and move away from manipulatives until students don’t need them to break apart words. Initially, you would use something like a bingo chip or cube, and move on to using letter tiles as students progress with this skill.
If students don’t quite understand what you mean by breaking up the word, tell them to stretch the word out into its individual sounds. This seems to help some who struggle with the concept.
Remember, students should already be familiar with segmenting compound words and syllables so the actual skill of splitting is not completely new for them! If you have not done that, then go back before segmenting phonemes.
My daily Phonemic Awareness Task Cards includes all these skills and more in the correct progression.
Phoneme Segmentation Activities
Elkonin Boxes
Elkonin Boxes are a great activity for introducing and practicing phoneme segmentation. It starts out with just two boxes, but then you can add more as students work with longer words.
Here’s how it works: First, the teacher says a word that contains one syllable, such as ‘me‘. The student repeats the word aloud then places a manipulative into the first box as they say the first sound in the word. In this example, the student would say /m/ and place a tile in the first box to represent that first sound. Then the student would say /ee/ while placing a manipulative in the next box.
From here you would move on to words with 3 sounds, like cat or play, and then words with 4 sounds, such as jump or clock. For each sound, a manipulative is placed in one box while the student makes that sound.
You can move from bingo chips to letter tiles, and eventually have students write the corresponding letters in the boxes. You want students to notice that sometimes 2 or 3 letters combine to make 1 sound.
Tapping Out Sounds
Tapping out sounds is a great skill that I teach my students to use all the time when working with new words. You don’t even need anything other than fingers to do this.
Have students tap on their arm or desk as they say each sound in a word. For example, with the word ‘dog‘, students would tap their thumb on the desk while saying /d/, tap their pointer finger on the desk while saying /o/, and tap their middle finger on the desk while saying /g/.
They could also tap their pointer, middle, ring, and pinky finger to their thumb instead of tapping a desk or their arms.
It doesn’t really matter what they tap, but that they use one finger for one sound.
You can also use worksheets and flashcards that have dots under or above each phoneme, as pictured above, and have students tap on each dot as they say the sound aloud.
Instead of tapping, you can also have students clap each sound to segment a word.
Below you can see how I use those popular Pop It toys on a worksheet to segment and write words. I have this worksheet available to download for free in my freebies library.
Phoneme Grapheme Mapping
This is very similar to Elkonin boxes but combines it with writing.
You say a word and/or show a picture of it. Students segment the word using manipulatives or by tapping out the sounds. Then students write the letters in sound boxes.
I have a free simple word mapping worksheet template that you can download in my freebies library.
You can also purchase my blank orthographic mapping templates from my shop to use with words that have 2 to 5 sounds. It’s a multisensory version with more steps than my freebie template (pictured above).
CVC Sliders
Using something like a pipe cleaner with stringing beads, students can slide a bead over for each sound while segmenting a word.
Call out words or hand students some picture cards that they can segment on their own. This could make a fun center if you created a deck of picture cards or worksheets.
Count the Sounds
Say a word aloud, or show students a picture, and ask them to count how many sounds are in the word. I like to give students a few picture cards and have them sort them into piles by how many sounds they have. Or I create cut and paste worksheets focusing on counting the sounds. The picture above is a worksheet from my Halloween Phonemic Awareness Activities set.
You can also use a Pop It fidget toy to count the sounds in words they hear.
Phoneme Jumping
This is a more kinesthetic activity, great for those wiggly students!
Instead of tapping or clapping, students would jump for each sound.
Students will hop while saying the sound out loud to segment the word. They can hop in place, or you can place a few targets on the floor much like sound boxes, for them to jump across one at a time as they sound out a word.
This last one can also help with directionality issues.
Phonemic Awareness Task Cards
If you’re looking for easy and quick ways to get phonemic awareness in your lessons then check out my no prep Phonemic Awareness Task Cards. These task cards include 124 cards that cover all areas of phonemic awareness, including phoneme segmentation, and can be done in just 5 minutes a day.
Bottom Line
Phonemic Awareness is a set of skills that are necessary to learn how to read. It’s important for children to develop the ability to segment words into their phonemes (unit sounds) in order to be successful readers, but this can often take time and practice. Try out these 5 phoneme segmentation activities to practice phoneme segmentation, and check out these other phonemic awareness activities to build phonemic awareness skills in developing readers.
Want to remember this? Save How to Teach Phoneme Segmentation to your favorite Pinterest board!
If you’re looking for more tips on teaching reading to struggling learners, check out these other posts:
- How To Structure Your Literacy Block
- Reading Assessments You Should Be Using
- What is the Science of Reading?
- Teaching High Frequency Words Using the Heart Word Method
- Phonemic Awareness Strategies & Activities For Struggling Learners
- The 6 Syllable Types
- Why You Should Switch To A Sound Wall
- Reading Strategies for Struggling Readers – Elkonin Boxes
- Activities That Improve Reading Fluency
Sharing is caring!
34 shares
- Share
- Tweet
Segmentation, noise reduction and phonetic analysis in the problem of speech recognition
References:
Muzychuk, D.
S. Segmentation, noise reduction and phonetic analysis in the problem of speech recognition / D. S. Muzychuk, M. S. Medvedev. - Text: direct // Young scientist. - 2013. - No. 6 (53). - S. 86-96. — URL: https://moluch.ru/archive/53/7058/ (date of access: 02.02.2023).
The article considers a speech signal segmentation and denoising algorithm based on the calculation of short-term energy and using qualitative threshold estimates to identify pauses. Calculations have shown that spectral centroids make it possible to efficiently determine noisy parts of a speech signal. A method for forming the grammatical form of a word based on its phonetic representation using Levenshtein and Damerau-Levenshtein metrics has been developed. Based on the recognition results, we can conclude that the Damerau-Levenshtein algorithm is best suited for obtaining the grammatical form of a word. nine0012
Keywords: speech recognition, segmentation, Damerau-Levenshtein metric, spectral centroid.
To improve the accuracy of calculating speech features and reduce the number of signal processing operations, it is necessary to separate “useful” information from pauses. There are various signal segmentation algorithms [2; 3]. Their common feature is the splitting of the signal into frames and their analysis. When processing speech signals, as a rule, they operate not with the original speech signal, but with its parameters calculated on the frame. The frame length is usually chosen so that its time duration is 10–20 ms, this is due to the fact that, due to the inertia of the articulatory organs in this interval, the signal parameters are practically unchanged [1]. In the study, speech segmentation was performed according to the algorithm proposed in [4]. It combines the calculation of short-term energy, but at the same time uses better threshold estimates to identify pauses in the signal. To illustrate the operation of algorithms in the Visual Studio environment in C# with integration with the MATLAB package, a speech-to-text conversion module was developed (Fig.
1). nine0009
Fig. 1. Application user interface. The word "OSA" is written down.
Let be the -th signal frame of length . Then its energy will have the following type:
(1)
The ratio (1) can be used to detect quiet periods in the signals. Below is the software implementation of calculating the signal energy in the MATLAB environment:
Along with energy in [31], the concept of spectral centride is introduced:
(2)
where-the coefficient of discrete waist-transformation of the sequence.
The spectral centroid is an important characteristic that allows you to evaluate the features of the sound. Experiments have shown that the spectral centroids in the sequence are extremely variable for speech segments.
Below is the implementation of the calculation of the spectral centroid of the signal:
The speech segmentation algorithm [4] is as follows:
1.
Calculation of the histogram of sequences of energies and centroids.
2. Applying a smoothing filter to histograms.
3. Detection of local maxima of histograms.
4. Let the quantities and be the positions of the first and second local maxima, respectively. Then the threshold value will be:
(3)
where is a user-defined parameter.
If the values of energy (1) and centroid (2) exceed the value of their thresholds (3), then the segment is considered voiced, otherwise we will deal with noise or an unvoiced gap.
To show the operation of the algorithm more clearly, let's open the speech signal (the word "OSA" spoken several times). After setting the segmentation parameters (Fig. 5-6) and pressing the "Segmentation" button, a segmented signal is displayed in the time domain. In this case, pauses, noises in the signal are highlighted in one color, and the “useful” signal, which is subsequently fed to the recognition block, is highlighted in a different color (Fig.
2). nine0009
Fig. 2. Display segmented waveform in the time domain.
Along with the graph of the segmented signal, additional information is displayed containing indirect data on the quality of segmentation (Fig. 3).
Fig. 3. Short-term energy and spectrum centroid.
At the output of the recognition block, after analyzing all the signal segments and deciding whether they correspond to specific phonemes, we obtain a character string characterizing the sequence of phoneme segments. The resulting chain is fed to the input of the word formation block to search for the grammatical form of the word. nine0009
The following sequence of actions is performed:
1. Formation of phonemes from repeating segments;
2. Formation of the phonetic form of the word;
3. Search for the grammatical form of a word.
Since the time interval on which the characteristics of a phoneme are calculated is less than its average length, the chain includes repeating segments from which separate phonemes are formed. For example, from the sequence "IIIVVAAAAA" it is possible to form the word "IVA" taking into account the elimination of repeating phonemes. nine0009
To form the correct phonetic form of a recognized word from the received string, it is necessary to have information about the most probable combinations of language phonemes. For these purposes, the database was implemented in the form of a text file that stores the probabilities of phoneme combinations. To assess the quality of the choice of phonemes, the frequency probability of their occurrence was used. In this case, the values of conditional probabilities of combinations calculated using the Bayes rule were used:
,0009
where is the conditional probability that a phoneme is followed by a phoneme ;
- frequency probability of occurrence of a phoneme;
- frequency probabilities of occurrence of phonemes.
When analyzing probabilistic characteristics using (4), the phoneme was chosen, the probability of which in a word was maximum.
For example, for the transcribed sequence VAFZA, since , then the decision was made: VAFZA → VAZA. At the output of the block for forming the phonetic representation of the word, we get a string, which, if interpreted correctly, will be the target form of the recognized word. nine0009
This form, synthesized from the analysis of the occurrence of phonemes, was compared with the words in the dictionary (Fig. 4). A fuzzy search algorithm was used, where the criteria for the proximity of words were the Levenshtein and Damerau-Levenshtein editorial distances.
Fuzzy search algorithms (also known as similarity searches) are the basis of spell checkers, as well as full-fledged search engines (like Google or Yandex). In addition, they are applicable to obtain the final grammatical form of a word in speech recognition. nine0009
Fig. 4. An example of a phonetic dictionary for comparison.
Levenshtein's algorithm is as follows. Let and - be two strings of length and respectively, then the Levenshtein distance is calculated by the formula , while the elements are:
5. Setting segmentation parameters. Levenshtein metric.
The implementation of the calculation of the Levenshtein distance was performed in the MATLAB environment and has the following form:
Fig. 6. Setting segmentation parameters. Damerau-Levenshtein metric.
The Levenshtein distance calculation algorithm was imported from MATLAB into C# and built in as part of the application's functionality for finding the closest dictionary form. The source code for the implementation of fuzzy search using the Levenshtein algorithm in C#:
7. The result of recognition of the word "OSA". Levenshtein metric.
For the Levenshtein distance, the set of elementary operations consists of the operation of replacing, inserting and deleting one letter. If we expand this set by the operation of permuting neighboring characters (provided that these characters are adjacent in both words), then we get the Damerau-Levenshtein distance. nine0009
The Damerau-Levenshtein algorithm considers transposition (permutation) as a single operation, along with insertions, deletions, and replacements. Most typing errors are transpositions, so this metric in some cases gives the best results in practice (an example is shown in Fig. 7–8). The code is shown below:
8. The result of recognition of the word "OSA". Damerau-Levenshtein metric.
In the process of work, a database of features of phoneme standards was built, a small set of words was taken for testing (20 words, 4–5 samples each). Some samples were deliberately noisy. The segmentation and denoising method described earlier showed high efficiency. This is due to the fact that the introduction of spectral centroids makes it possible to more qualitatively determine the segments containing noise, since the level of the centroid in the noisy sections of the signal is significantly lower than in those that carry "useful" information.
The average coefficient of word recognition using the Levenshtein metric was 91.5%, Damerau-Levenshtein - 96%. From this we can conclude that the Damerau-Levenshtein algorithm is most preferable in order to form the grammatical form of a word.
References:
1. Mikhailov V. G., Zlatoustova L. V. Measurement of speech parameters. - M .: Radio and communication, 1987. - 168 p.
2. Flanagan J.L. Analysis, synthesis and perception of speech / transl. from English A. A. Pirogov. M.: Communication, 1968. 397 p.
3. Vishnyakova O. A., Lavrov D. N. Application of the Hilbert-Huang transform to the problem of speech segmentation // Mathematical structures and modeling. - 2011. issue. 24. - p. 12–18
4. T. Giannakopoulos, “Study and application of acoustic information for the detection of harmful content, and fusion with visual information,” Ph.D. dissertation, Dpt of Informatics and Telecommunications, University of Athens, Greece, 2009.
Basic terms (automatically generated) : MATLAB, grammatical word form, algorithm, short-term energy, fuzzy search, speech signal, segmented signal, word , spectral centroid, time domain. nine0009
Segmentation Crash Course for Marketers
When was the last time you felt annoyed when you saw a mobile ad because it wasn't relevant or because it was popping up for the 87th time? Probably not long ago...perhaps even today.
In addition to wasted budget, the image of this brand suffered. And all because the marketing team was unable to properly target ads.
To build the right audience on a mobile platform, you need to understand the nuances well. The mobile audience is constantly changing, and the variety and scope of data in this environment is enormous. nine0009
How to cope with this difficult task?
There is a solution: Segmentation.
Audience segmentation is the key to using the right data points across millions of users and turning them into installs, engagement and revenue.
Types of mobile segmentation
What is mobile audience segmentation?
This is the creation of a group of potential and/or existing users based on specific general criteria that ads can target.
nine0009
Segments can be incredibly detailed, depending on the platform you're working with. In audience segmentation, data is the key to success. In each subgroup you create, you can test different variations of personalized messages to determine which ones work best.
Once you master segmentation, every level of user interaction and every point of contact will be based on thoughtful segmentation.
There are many ways to segment an audience, but the most common approaches are summarized below:
- Geographically: country, state, city, and sometimes a specific zip code. Geographic mobile data is based on what users have entered in the app, such as users of the Facebook app who have indicated they live in San Francisco.
- By location: based on the device's GPS signal, where the user is actually located at the time the ad is displayed, for example, the user is within a certain radius of the storefront. nine0202
- Demographics: gender, age, income, e.
g. women aged 50 and over with an income of more than $200,000. - Psychographically: values and interests, such as users who regularly install and interact with news apps.
- Technological principle: every mobile device has a small technological stack, for example, an iPhone 11 user with a device with at least 4 GB of RAM. nine0202
- Behavioral: what users do on their phone, for how long, and what are the results, such as mobile gamers who typically play 20 minutes or more per session and have made at least 2 in-app purchases.
Let's look at audiences that were created by combining geographic and behavioral data.
This example combines behavioral options:
Targeting options on top media platforms
Now that we've covered the possibilities of segmentation, let's look at targeting options on popular media platforms.
Facebook's targeting capabilities are considered unmatched.
The social network offers three options for creating custom audiences:
- Basic audiences: Allows you to set parameters for location, behavior, demographics, connections and interests
- 0020 : Uses your own data extracted from your CRM and/or email lists, your website visitors, or existing users of your app (for the latter you use the Facebook SDK)
- Lookalike Audiences your clients.
Facebook Audience Dashboard:
App campaigns in Google are mostly automated, but you can define geo-location and languages for targeting, and upload lists for re-engagement campaigns. Everything else happens automatically. nine0009
Apple Search Ads use keywords to enable marketers to serve ads to users with high search intent. These can be general or exact matches. The platform also allows targeting by geolocation, age, gender, customer types (everyone, new, returning, users of my other apps) and device type (only iPhone and iPad of course).
Marketing Segmentation Options Twitter : You can segment your audience by age or gender, @user (the people and brands they follow), interests, conversations. nine0009
IronSource - a network that serves game developers - offers a segment guide where you can learn how to segment based on device technical stack (network connection, device model, OS version) and monetization data (for example, if users spend on purchases in the app, and if so, how much they spend). Snap also has a number of options for segmentation, from predefined audiences to custom audiences that include lookalike audiences and tools that match Snapchat users with your own data by device ID or email list. nine0009
Get the latest marketing news and insights from experts straight to your inbox!
Using attribution data for user segmentation
Attribution data is a building block for all types of segmentation. It covers the path to installation - organic or inorganic - and the subsequent activities after installation.
For example, attribution data might tell a marketer that 1,000 app installs were generated by a Twitter campaign and the total revenue generated by that group was $2,000 (or $2 per user). nine0009
Attribution data is mainly used to segment re-engagement campaigns (after all, you're working with data about your own users that you already have). Since it is difficult for most applications to guarantee continued use, re-engagement – whether paid remarketing or custom media campaigns (email, push notifications, blog, cross-promotions, social media, etc.) – becomes an extremely important activity. In fact, between 2017 and 2019the share of apps with remarketing campaigns almost doubled and the share of remarketing conversions (among all conversions - remarketing and non-organic installs) increased more than 2.5 times:
The growing importance of re-engagement once again highlights why accurate segmentation is a key success factor mobile marketing.
You can also use attribution data to run user acquisition campaigns with similar audiences.
These are the audiences that reflect your valuable users. nine0009
Lookalike targeting will only become more reliable as other networks meet demand. However, to be effective, significant resources and a significant amount of data are required.
Want to take your re-engagement and user acquisition campaigns to the next level?
Here are some ways to benefit from user attribution and segmentation:
| Goal | Method | Audience |
|---|---|---|
| Drive users from your own media to specific touchpoints in your funnel. | Send a push notification that directs the user to an application page with an offer to collect a reward in order to help players progress in the game. | Users who made at least one in-app purchase within the first two weeks of installation and which have been inactive for two weeks. |
| Recruiting players from game 1 to game 2 with cross promotion. | Placement of an advertising banner in Game 1 to promote the installation of Game 2. | Previously active users (with 2+ sessions per day) whose activity has dropped (for example, 1 session every 3 days after two weeks of installation) |
| Increasing loyalty among one-time shoppers in shopping apps | Set up retargeting one-time smartphone purchasers with a related product discount | First-time purchasers in a specific product category (e.g. smartphones) |
| Set up retargeting travel app users for the vacation planning period. | Segmentation based on exclusion parameters (very handy and important strategy) - you can exclude deletions, active users, etc. nine0318 | Include users who booked a trip during the previous holiday season; Exclude active users, those who uninstalled the app, and users who have already booked a trip during the holiday season. |
| Finding the best creative for your holiday shopping campaign | Split testing audiences is a must to identify and test the best-performing messages, creative, and offers. | Show Creative A to Audience A, then show Creative B to Audience B (same characteristics as Audience A) |
Attribution data is critical to effective mobile segmentation, but before contacting a provider, be sure to find out what to look out for in terms of security, accuracy, and continuity—all critical factors for any ambitious mobile marketing plan .
Privacy:
You can't talk about mobile audience segmentation without talking about privacy and industry-standard restrictions. nine0009
We live in a privacy-sensitive world and it is your job to work with vendors and media partners that comply with all standards and implement standard data protection measures in accordance with the GDPR and CCPA (General Data Protection Regulation adopted by the European Union).
, and the California Consumer Privacy Act, respectively).
Even if your company is not headquartered in the EU or California, these laws will almost always apply because you will always have California and EU users. Always stick to the fine print in your own user agreements - that's your legal responsibility. nine0009
But it's not just about what you do with user data, it's also about how much data you disclose to your partners. Attribution providers and platforms should allow you, the app marketer, to control the data that is sent to ad networks. For example, sending media partners only the IDs of the devices you want to segment.
Imagine a scenario where your application sells drugs. If you want to reach out to a group of users who have previously purchased drugs through your app, you can submit a list of device IDs to an ad network without disclosing the fact that they purchased drugs. Device IDs are all that an ad network needs. nine0009
Ultimately, any personal information (such as name, phone number or email address) should only be shared with the user's express consent.
Mobile marketing is not only responsibility, but also savvy. There are no-go areas like selling data to third parties, but there are others like sending push notifications when your user is not in the product (assuming the user has given consent).
Output: You can very successfully segment data without sharing it with third parties.
8 Tips for Effective Segmentation
Now that you've completed your Crash Course on Audience Segmentation, here's a checklist for your next campaign:
- Do continuous testing across multiple ad networks with new segments (never stop testing!).
- Perform incremental segment tests for finer optimization down to message types, offer types and ads. nine0390
- Use detailed engagement data such as in-app events (post-install actions that mark engagement, such as in-app purchases, completing a tutorial, leveling up a game, placing an item in a cart) to create more targeted audience segments.
Tip: Make sure you're measuring the right number of events for your vertical!
- Consider both mobile and web audiences in your segmentation strategy For web users, create segments that will eventually lead users to the mobile app as this is the best performing consumer touch point, just like smart banners . nine0390
- Combine personalized messaging with deep links for each segment to create a truly relevant, memorable customer experience.
- Set a frequency limit for campaigns that send a specific segment and/or user. (There is such a thing as too much marketing in a segment, and the last thing your brand needs is for existing and/or potential users to get tired. If a user gets tired of seeing you, it will be difficult to bring them back.)
- Consider creating new segments from existing segments using CRM or an internal business intelligence tool.
- Make regular communication between your marketing and product teams a top priority so everyone stays on target and avoid unnecessary campaigns that push users away.
Learn more
