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url="https://www.youtube.com/embed/J3hjHssSZ1A?si=M-Cpm4tBjpXY98fX"
/>

## What is ROMI?

**ROMI, or Return on Marketing Investment**, is a metric used to evaluate the effectiveness and profitability of marketing campaigns or activities.

It shows whether the investments generate revenue. The uniqueness of ROMI lies in its focus specifically on marketing investments, not on general investments.

The formula for calculating ROMI varies depending on the specific conditions and goals of the campaign. Typically, ROMI is calculated as follows: first, subtract the marketing costs from the revenue generated, and then divide this result by the marketing costs. The metric is expressed as a percentage.

**ROMI formula:**

<Img
img="/uploads/romi_formula_d3107781a2.jpg"
alt="ROMI formula"
/>

**Example of calculating ROMI in practice:**
*ROMI = (revenue $200,000 from the marketing campaign – costs $50,000 on the marketing campaign) / costs $50,000 on the marketing campaign = 3 or 300%.*

## How ROMI helps advertising strategy

Knowing ROMI gives an understanding of whether you are incurring losses or making a profit compared to the amount spent on marketing. You can also calculate the earned amount.

Additionally, it helps in planning the marketing strategy for the next quarter. It is recommended to review the metric every three months.

<Img
img="/uploads/romi_in_marketing_strategy_planning_cc9d566731.jpg"
alt="ROMI in marketing strategy planning"
/>

It is essential to determine the effectiveness of current efforts and their impact on your company in the future. You can allocate time more wisely and increase the productivity of the marketing team. Or direct resources where they will be most productive.

Return on Marketing Investment is considered good if it is above 100%. This indicates that the money invested in marketing generates profit.

### Who should pay attention to ROMI

ROMI is a metric that various business stakeholders should pay attention to:

**1. Marketers.** They evaluate the effectiveness of campaigns and other marketing activities. This helps them understand if the chosen strategies are profitable, make data-driven decisions, and allocate resources wisely.

**2. Executives.** They determine the overall efficiency and profitability of the marketing department. With ROMI, they justify marketing budgets and identify the priorities of different activities. ROMI also shows the impact of various campaigns on the organization’s financial performance.

**3. Finance department.** They focus on ROMI to assess the profitability of marketing investments and how it aligns with the organization's financial goals. ROMI helps in proper budget allocation and tracking effectiveness.

**4. Business owners and leaders.** ROMI provides insights into how effective marketing efforts are in driving company profits. Through ROMI, they evaluate the overall strategy and determine investments in activities.

**5. Stakeholders and investors.** They need to understand the effectiveness of marketing campaigns as it impacts the financial performance and growth of the organization. Through ROMI, they assess the contribution of the marketing department to the company's profitability and market position.

## ROMI for short-term and long-term effectiveness

### Short-term effectiveness

It provides insight into how marketing campaigns and other initiatives instantly impact profit. By comparing the revenue from a specific campaign with its costs, marketers evaluate short-term profit and the effect of their efforts. This way, they analyze which campaigns bring quick results and adjust their strategies accordingly.

*For example, a company launches a short-term advertising campaign during the holiday season, spending $10,000 on ads. This brings in $30,000. The ROMI for this campaign is: ($30,000 - $10,000) / $10,000 = 2 or 200%. This means that for every dollar invested, the company earned $2 in profit in the short term.*

### Long-term effectiveness

By tracking the impact of ongoing investments over an extended period, marketers determine the success of long-term strategies aimed at increasing revenue and customer acquisition.

*For example, a company invests in brand positioning and customer retention programs throughout the year, spending a total of $500,000. By the end of the year, the company's revenue increased by $2 million. The ROMI for this long-term marketing strategy is: ($2,000,000 - $500,000) / $500,000 = 3 or 300%. This indicates that for every dollar invested, the company earned $3 in profit over the year.*

Considering ROMI for both short-term and long-term effectiveness, marketers gain a comprehensive understanding of the efficiency and profitability of their marketing activities.

## ROMI compared to other marketing metrics

ROMI provides valuable insights into the financial impact of marketing activities on company profits by calculating the return on marketing investments. However, it is important to consider other metrics to get a full picture of marketing effectiveness.

ROI, CAC, CPL, CLTV, and conversion rate—all these metrics offer additional perspectives on various aspects of marketing performance. By analyzing multiple metrics, marketers can make informed decisions, optimize strategies, and allocate resources efficiently.

Let’s compare ROMI with other marketing metrics, highlighting their unique features and how their combination helps assess marketing effectiveness.

### ROMI vs ROI

**ROMI (Return on Marketing Investment) and ROI (Return on Investment)** are interrelated metrics used to evaluate the financial performance of marketing efforts, but they differ in focus:

- ROI is a general financial metric that measures the total return generated from any investment, including marketing. 

- ROMI calculates the return specifically on marketing investments.

<Note>
How to calculate ROI? Why is this metric important for businesses? Read the article "ROI: What It Is and Who Benefits from It."
</Note>

### ROMI vs ROAS

**ROAS (Return on Advertising Spend) and ROMI (Return on Marketing Investment)** are metrics for measuring the effectiveness of marketing efforts, but they focus on different aspects of performance.

- ROAS is a metric used in online advertising to evaluate the revenue generated from advertising investments. It measures the efficiency of specific advertising campaigns by calculating the revenue per dollar spent. 

- ROMI is a broader metric that encompasses all marketing investments, not just advertising spend.

<Note>
For more details on the differences between ROI, ROMI, and ROAS, read the article [**"What is the Difference Between ROI, ROMI and ROAS."**](https://altcraft.com/blog/roi-romi-roas-difference)
</Note>

## Advanced approaches to ROMI

Understanding ROMI leads to creating a more sophisticated strategy than relying solely on ROAS and ROI metrics.

It's important to know that not all investments are equal. Sometimes, it's essential to understand how different marketing goals perform in terms of profitability.

In one of his books, Rex Briggs introduced the term ROMO (Return on Marketing Objectives) to encourage marketers to use different metrics for various tasks. Not all of these tasks are profit-related; some focus on brand awareness or perception.

It is possible to break down ROMI calculations by campaign or by where these campaigns are in [**the sales funnel**](https://altcraft.com/blog/what-is-sales-funnel) (top-of-funnel activities like social media tags or organic website traffic; bottom-of-funnel activities like sales and conversions).

## Frequently Asked Questions: ROMI

### 1. Why is ROMI becoming a popular metric among marketers?


ROMI measures marketing effectiveness comprehensively, enabling better resource allocation and budget optimization. It requires justifying marketing expenditures and is easily calculated using digital tools.

### 2. How often should ROMI be analyzed?

The frequency of ROMI analysis depends on various factors: goals, business nature, and campaign duration.

For short-term activities, ROMI can be analyzed weekly or monthly to capture fleeting trends and adjust strategies promptly.

For longer-term marketing efforts, quarterly or annual ROMI analysis is suitable.

### 3. In which industries is ROMI more important?

ROMI is valuable across different industries and sectors. For example:

1. Retail companies often invest significantly in marketing to attract traffic, increase sales, and strengthen [**customer loyalty**](https://altcraft.com/blog/customer-loyalty). 

2. Consumer packaged goods (CPG) companies heavily rely on marketing to promote their products, enhance brand popularity, and boost sales. 

3. Telecommunications companies invest substantial resources in marketing to acquire new customers, retain existing ones, and promote services. 

4. In the financial sector, marketing plays a crucial role in acquiring new clients, building trust, and promoting various services. 

5. Technology-related companies often allocate substantial funds to marketing efforts to build brand awareness, [**generate leads**](https://altcraft.com/glossary/what-is-lead-generation), and advertise products.

### 4. How to improve ROMI?

Improving return on marketing investment requires strategic planning, data analysis, and continuous optimization. Here are several strategies to help improve your ROMI:

**1. Set specific and measurable goals.** This focuses efforts on priorities and tracks progress effectively.

**2. Analyze data.** Monitoring [**key performance indicators (KPIs)**](https://altcraft.com/glossary/key-performance-indicators-what-they-are-and-how-to-use-them), conversion rates, customer lifetime value (CLV), and cost per acquisition identifies areas for improvement.

**3. Segment your target audience.** Analyzing and segmenting the target audience tailors advertising messages and strategies to specific customer groups.

**4. Test and optimize your campaigns.** Conducting A/B testing adjusts messages and enhances communication channels.

**5. Invest in marketing analytics.** Utilize tools and platforms for analytics to delve deeper into data and optimize your efforts.

**6. Foster collaboration between marketing and finance departments.** Improve communication and cooperation between your teams.

**7. Adjust marketing spend.** Regularly analyze the results of various marketing channels and campaigns to determine where your advertising investments yield the highest ROMI.

**8. Focus on customer retention.** Enhance customer retention processes to maximize the value of your initial customer acquisition investments and thereby improve ROMI.


### 5. Can ROMI be calculated for both online and offline marketing campaigns?

Yes, it can. Whether it’s an online campaign aimed at driving website traffic and conversions, or an offline campaign targeting a local audience through billboards, ROMI shows the return on marketing investments.

Tracking ROMI for both online and offline campaigns provides insight into which channels and strategies deliver the highest return on investment. This information helps optimize marketing budgets, make data-driven decisions, and allocate resources effectively to maximize overall ROI.

### 6. How does seasonality affect ROMI?

Seasonality has a significant impact on ROMI. Consumer behavior often changes depending on the time of year due to various factors such as holidays, weather, and cultural events.

Companies should consider the following factors:

**1. Consumer demand.** Aligning marketing efforts with [**peak seasons**](https://altcraft.com/blog/seasonal-marketing) can capture increased consumer demand.

**2. Marketing planning.** Marketers plan campaigns and allocate resources effectively by identifying key events that influence customer behavior.

**3. Budget allocation.** Adjusting marketing spend according to peak seasons and aligning budgets accordingly optimizes ROMI during high-demand periods.

**4. Campaign timing.** Considering seasonality, marketers plan and execute campaigns during periods of highest interest and engagement from customers.

**5. Channel selection.** Different seasons may require specific channels. Companies choose the appropriate communication methods for particular seasonal campaigns by analyzing past customer behavior.


Source: [Ignite Visibility](https://ignitevisibility.com/romi-vs-roas-marketing-metric/)

ROMI measures the profitability of marketing investments, guiding strategy optimization, budget allocation, and resource distribution for maximum ROI.

medium_data-analysis-romi.png

small_data-analysis-romi.png

thumbnail_data-analysis-romi.png

data-analysis-romi.png

About what ROMI means in advertising and how to calculate it correctly.

ROMI, What It Is, Its Impact on Marketing Strategy

Data Analysis: ROMI and Its Impact on Strategy

<Img
img="/uploads/what_is_statistical_power_a8256e4185.png"
alt="What is statistical power"
/>

Defining statistical power or the "sensitivity" of a test is an important part of planning before launching A/B testing. It allows you to introduce more positive changes to a website and increase your revenue.

## Definition of statistical power

**Statistical power** is the ability of a criterion to detect differences where they actually exist. The sensitivity of a test will show a significant result and reject the incorrect null hypothesis.

Before delving into the elements of statistical power, it's essential to understand the types of errors that can occur in tests and how to prevent them.

## Two types of errors

### Type I error

**Type I error** is a false positive result: rejecting the null hypothesis that is actually true.

**The null hypothesis** states that there is no difference between a pair of events or phenomena.

A false positive test shows that there is a difference between the compared variants, even though there isn't one in reality. Such a situation can occur due to an error in the test or if randomness creeps in.

The probability of a type I error is denoted by the Greek letter alpha (α) and indicates the threshold level at which an A/B test should be trusted. If a test is reliable at 95%, the remaining 5% represents the probability of a type I error.

If a business cannot tolerate a 5% chance of error, considering it too high, the probability of false positive results can be reduced by increasing the test's reliability. If the reliability is increased to 99%, the probability of a type I error decreases from 5% to 1%. However, there are some "pitfalls" in this calculation.

The decrease in the probability of type I errors increases the probability of type II errors because there is an inversely proportional correlation between these values. Therefore, reducing the alpha error level (for example, from 5% to 1%) ultimately reduces the overall statistical power of the test.

<Img
img="/uploads/types_error_36faa5eac7.png"
alt="Graph by statistical power"
src="Type I error and its proportional connection with type II error"
/>


Sometimes it's necessary to consciously increase the risk of type I errors (for example, from 5% to 10%) to achieve high test sensitivity.
### Type II error


**Type II error** is a false negative result, meaning the inability to reject a false null hypothesis. Such a test will not show the advantage of any of the options, even though there are options that are more effective than others.

The probability of a type II error (β-error) is inversely proportional to statistical power (1 - β). If the risk of a Type II error is 20%, the sensitivity of the test is 80%.

Since alpha and beta errors have an inversely proportional relationship, if a test has an extremely low alpha error value (for example, 0.001%), the risk of a type II error is very high.

Finding a balance is not an easy task. Through A/B testing, a business aims to determine the best combination of elements and identify the most effective variant among many. If the tests lack sufficient sensitivity, there's a great risk of not noticing a genuinely good option.


## Variables affecting test statistical power


Next, we'll examine four variables that influence the statistical power of a test. While studying these variables, it's important to remember that the primary task of a tester is to control the probability of errors. This can be achieved through:

- sample size; 

- minimum detectable effect (MDE); 

- significance level (α); 

- desired level of power (implied Type II error coefficient).


### Sample size


The sample needs to be sufficiently large; otherwise, conducting a good split test is impossible. It's crucial to calculate the sample size to ensure it's of the necessary and adequate size. A small sample won't provide enough test power, while an excessively large one will extend the test duration (long tests are more expensive and time-consuming).

Each analyzed variant or [**segment**](https://altcraft.com/help-center/manage-customer-database/database-segmentation-for-email-newslettering) should have a significant number of users. For this reason, the sample size should be planned in advance, ensuring that the test always has good statistical power. Otherwise, exiting the test might reveal too many variants or segments. If this realization comes too late, the test will have been conducted in vain.

Expect statistically significant results within a reasonable period (e.g., no less than a week or a business cycle). In most cases, it's recommended to conduct testing for 2-4 weeks. Any duration exceeding this might lead to sample contamination and issues with cookie file deletion.

Therefore, determining the sample size and testing duration beforehand is crucial. This helps avoid the common mistake of conducting A/B testing "blindly," ending before obtaining statistically significant results.


### Minimum detectable effect (MDE)


MDE is the difference between variants that needs confirmation. Small differences are hard to detect, requiring very large samples. Substantial differences are noticeable even in small samples.

However, there exists a paradox where small samples can be unreliable, leading to erroneous tests. There's always a risk of error, and there's a narrow range within which results can be considered reliable. Since there's no single rule for determining sample size, all nominal levels are not 100% reliable.

### Significance level


Testing results are considered statistically significant under the condition that the null hypothesis is incorrect.

In simple terms, if during a split test, one landing page can confidently be preferred over another (with a test confidence level of 95%), there's only a 5% chance that the observed improvement is due to chance or error. Conversely, there's a 95% chance that the observed difference is not due to chance.

The 5% significance level is the most commonly used initial level in online testing, representing a 5% chance of a type I error, as mentioned earlier. An alpha of 5% means there's a 5% chance of incorrectly rejecting the null hypothesis.

Under equal conditions, reducing alpha from 5% to 1% simultaneously increases the likelihood of a type II error. An increased risk of a type II error reduces the validity of the test.

### Desired level of power


If the statistical power of a test is 80%, there's a 20% chance that the real difference between variants won't be detected. If a 20% chance is too risky, it can be reduced to 10%, 5%, or 1% to achieve powers of 90%, 95%, or 99%, respectively.

Before concluding that a 95% or 99% power analysis will solve all your problems, note that each increase in analysis power significantly increases sample size and analysis time.

So, what level of test sensitivity do you really need? The acceptable level of false negative risk for conversion optimization is often considered to be 20%, corresponding to an analysis power level of 80%.

There isn't a universally recognized standard for 80% power, but it represents a reasonable balance between alpha and beta errors. Consider the following:

- What is an acceptable level of risk for missing out on a quality improvement for you? 

- What is the minimum sample size required for the test to achieve the desired sensitivity?


## Calculating the statistical power of a test

You can use an A/B test calculator. Enter known variable values to determine, for example, the required sample size to achieve sufficient test sensitivity.

For instance, you calculated that testing each variant requires a sample size of 681 individuals. Your calculations were based on the following initial values: test power of 80%, alpha of 5% (95% statistical significance). You also knew the conversion rate for the control group is 14%, and you assumed an expected conversion rate for the better variant of 19%.

<Img
img="/uploads/calculate_the_statistical_power_of_a_test_848e9bae89.png"
alt="Calculation of the sample size"
src="How to calculate the statistical power of a test"
/>


Using the same initial data, knowing the sample size, alpha value, and the desired level of statistical power (for example, 80%), you can determine the minimum detectable effect (MDE).

<Img
img="/uploads/determination_of_mde_f4e64ce91b.png"
alt="Calculation of the minimum detectable effect (MDE)"
src="Determination of MDE"
/>


As we can see, an A/B test calculator is quite convenient when you know the values of three variables and aim to find the fourth one.

## What to do if the sample size cannot be increased?

In some cases, there's a need for a more high-powered test, but increasing the sample size is not possible, such as when the page traffic is too low.

Let's consider a scenario: you've inputted your data into the A/B test calculator, and it shows that a sample size of 8000 or more is required.

<Img
img="/uploads/use_a_sample_size_calculator_34fab3e500.png"
alt="Determining the required sample size"
src="How to use a sample size calculator"
/>

But you don't have the ability to gather such a sample; it would take several months or even longer. In this case, you need to increase the minimum detectable effect (MDE). In the example below, increasing the MDE from 10% to 25% reduces the sample size to 1356.

<Img
img="/uploads/sample_size_depends_on_increasing_the_mde_1aac156651.png"
alt="Increase the MDE to get the desired sample size"
src="Sample size depends on increasing the MDE"
/>


Is it really always possible to increase the MDE to 25%? After all, this will reduce the quality of the test.

The second option comes into play if you're willing to accept a 10% risk of type I error. In that case, the optimal option would be to decrease the test's significance level to 90%.

<Img
img="/uploads/balance_between_errors_within_calculator_1d200f99e4.png"
alt="Reducing the level of confidence to obtain the required sample size"
src="Balance between errors within sample size calculator"
/>

Iterating through data in hopes of obtaining satisfactory figures isn't the most reliable strategy. Analysts recommend starting by determining the sample size and then arbitrarily adjusting other values until the results are deemed satisfactory.

## Conclusion

Statistical power helps avoid errors during testing and identifies genuinely impactful performance factors on your platforms.

Adhere to these simple guidelines:

- Choose the test duration wisely: ideally 2-4 weeks 

- Use a test calculator to determine an acceptable test power 

- Adhere to the requirements for the minimum sample size 

- Aggregate segments and test them if needed 

- Set sensitivity test requirements only after meeting the minimum sample size requirements.

Source: [**CXL**](https://cxl.com/blog/statistical-power/)

Statistical power gauges a test's ability to detect differences. It helps avoid false conclusions by assessing the test's sensitivity to find genuine changes.

medium_what-is-statistical-power.png

small_what-is-statistical-power.png

thumbnail_what-is-statistical-power.png

what-is-statistical-power.png

About how to correctly calculate the statistical power for testing.

Statistical Power: Definition, How to Calculate & Variables 

What is Statistical Power

<Img
img="/uploads/image3_e1a68879d7.png"
alt="RFM Analysis: What It Is and How to Do It"
/>

There are two main ways to work with customers: attracting new ones and communicating with existing ones, motivating them to make repeat purchases. The second option is cheaper, but it is important to understand how to communicate with the customer base to generate more revenue.

This is where RFM analysis comes in. It evaluates users based on the sum, frequency, and recency of purchases. As a result, marketers can understand which category of customers to make individual offers to, which customers to show additional products to, and which customers to reactivate. This simple method is used to find the most profitable customers and those who are at risk.

In this article, we will look at what RFM analysis is, its advantages and disadvantages, how to conduct it manually and with the help of technical tools.

## What is RFM analysis and why to manage it


RFM analysis is a [**customer segmentation technique**](https://altcraft.com/blog/how-and-why-to-segment-target-audience) based on their purchasing behavior. The goal is to identify the most valuable customers who bring the company the most revenue. Typically these are loyal customers. 

The database is divided into three categories based on:

- **Recency:** the time since the last purchase. Different products may have different recency thresholds. For example, if a customer last bought your brand of shampoo six months ago, it is significant because shampoo is a consumable product. However, buying winter tires for a car one year ago is not considered an old purchase because tires are typically used for more than one season.



- **Frequency:** how often a customer makes purchases.



- **Monetary value:** how much a customer spends on the company's products.


RFM analysis is reuired to develop an action plan and communication strategy for each user category.


RFM is compared to the Pareto principle, where 20% of efforts give 80% of results: loyal customers bring in more money than new and casual customers, although marketing budget is also spent on them. Therefore, customer analysis is important for cleaning up the database and redistributing resources to avoid wasting efforts.


Additionally, RFM segmentation is useful for planning future advertising campaigns, specifically for:


- personalizing offers;



- reminding customers about a new purchase if the previous one was a long time ago;



- presenting [**additional products**](https://altcraft.com/blog/what-is-cross-selling) when a customer orders at a low price point.


This way, the company spends less on increasing its customer base, as the existing audience is profitable.


## Pros and Cons of RFM Analysis

### Pros:


- **Opportunity to gather data for predicting customer behavior.** If you know that a group of customers makes purchases every 2-3 weeks with a low average check, you can remind them of a new purchase when the time comes.



- **RFM analysis is a helper for personalization.** If a customer does not make high-check purchases, there is no need to offer them expensive products.


- **Savings in marketing budget.** Attracting and warming up new leads is expensive, while working with existing ones reduces costs.


- **Selection of an audience for advertising campaigns.** Based on historical RFM data, marketers can predict which offers a customer will respond to and which ones they will ignore. If a user only chooses luxury segment goods, showing them advertising of a low-cost product is not effective.


- **Easy analysis, which does not require special efforts and skills.** Only time is needed for data collection and processing, if you do segmentation manually. RFM [**automation**](https://altcraft.com/en) tools make the process faster.

### Cons:


- **RFM analysis of the customer database does not take into account the context of purchases (such as seasonality) and customer characteristics (such as demographics).** Such data is also important for offering products.



- **RFM segmentation will not bring results when there is a small amount of initial data.** The database for analysis should be at least ten thousand people.


- **It is not suitable for companies that sell large and expensive products.** For example, real estate is usually purchased once or twice in a lifetime, so customers are unlikely to return for a new apartment after a year.


- **Data has to be updated and the analysis repeated, as customers can move from one segment to another.** The standard period is once every six months, but if there are rapid changes in sales, it is worth doing it once every two months.

## How to do RFM analysis

### Step 1. Collect the database


Data can be accumulated in *Excel*, *Google Sheets*, or marketing management systems. The database for analysis should contain customer names, contacts, information on frequency, recency, and the amount of all purchases made by that person.

### Step 2. Indicate the scale for segmentation


Each of the three RFM criteria is ranked on a scale from 1 to 3 (a standard variant). Before the analysis, a description for the ratings should be made. An example is given in the table.

<Table>
|Criterion | Description of the scale |
| ------ | ------ |
| Recency | 1 – the client made a purchase long time ago; 2 – average recency; 3 – a recent purchase |
| Frequency | 1 – buys seldom; 2 – average frequency of purchases; 3 – buys often |
| Monetary value | 1 – insignificant amount of money; 2 – average amount; 3 – expensive purchases |
</Table>


Sometimes criteria can be further detailed. For example, for the description "the client made a purchase a long time ago", it is convenient to add a specific time frame, such as "1-2 years since the last purchase", and so on for the others.


The scale from 1 to 3 is considered standard, but it can be expanded to 10. However, this will increase the number of segments, and such an RFM analysis will take more time.


### Step 3. Evaluate each customer by RFM


This is the most time-consuming step if the analysis is done manually. Using the chosen scale, assign scores to each user based on recency, frequency and monetary value.


As a result, each customer receives a three-digit score. For example, a customer with an RFM score of 221 has made purchases relatively recently, occasionally returns to make purchases, but spends very little.


*Here’s an example of the table for evaluation.*


<Table>
| Client | R | F | M | Specification |
| ------ | ------ | ------ | ------ | ------ |
| Client A | 1 | 1 | 2 | A customer who buys seldom and with long intervals between orders. Brings an average amount of money. |
| Client B | 2 | 2 | 1 | This customer purchases with average frequency and without particularly long gaps. They only choose inexpensive items. |
| Client C | 3 | 3 | 3 | It’s the most profitable customer who makes high-cost purchases frequently and with a short time lapse between them. |
| Client D | 1 | 3 | 2 | They haven't made purchases in a while, although previously ordered frequently with a decent average order value. |
</Table>


### Step 4. Divide segments into groups


With a scale of 1 to 3, there can be 27 segments. Similar ones should be combined into a single one to simplify the work.


Possible groups of customers:


- **Most profitable.** They buy a lot and often, are loyal and with a sufficient LTV. The highest score in the category is 333.



- **Regular customers with average performance.** They do not always buy often and a lot, but they are loyal to the brand and still come back. The highest score in the category is 222.



- **Risk group.** People who bought only once or order very rarely and spend little. The most unprofitable customer will receive a score of 111.


### Step 5. Make a decision


After evaluating and classifying customers, marketers decide which approach to take for each group.


For example, the most profitable customers can be offered exclusive loyalty conditions (333, 332, and so on). An activation email campaign can be sent to users who haven't made a purchase for a long time (111, 112, and others). For regular customers with a small average purchase amount (such as 321), additional products and conditions can be selected [**to encourage them to buy more**](https://altcraft.com/blog/5-marketing-techniques-to-push-customers-to-buy).


## RFM-analysis in Altcraft Platform


[**Altcraft Platform**](https://altcraft.com/en) is a platform that automates RFM analysis and collects data on the context of purchases. The tool collects information on the frequency, recency and monetary value of orders, as well as on the activity of customers across different channels (SMS, email, push notifications, and others). Additional data on the opening of promotional mailings, actions in applications and on websites, and participation in [**loyalty programs**](https://altcraft.com/blog/customer-loyalty-program) will indicate what led the customer to make a purchase and what hindered it.


The purchase amount for analysis is tracked on external resources: websites, social networks, applications, CRM. Altcraft Platform pixels record the completion of goals (for example, making a purchase at a certain value), which are configured inside the platform. The monetary value selection condition for the RFM analysis scale is also set here: total goal value is less than/greater than/equal to a certain value N.


<Img
img="/uploads/image2_0f67217d32.png"
alt="Configuring based on monetary value criterion"
src="Configuring based on monetary value criterion"
/>

The frequency of actions and the duration between them are tracked. To do this, goal completion parameters are set. For example, purchases that occurred at least once within twenty days are taken into account in the analysis.

<Img
img="/uploads/image4_78130f29ff.png"
alt="Configuring based on frequency of action"
src="Configuring based on frequency of action"
/>


Next, segments are created based on the data. Customers are assigned to a particular segment based on the following criteria:


- whether the user currently meets the selection criteria now or met the criteria in the past;



- how many times during a period the customer met the specified criteria.



<Img
img="/uploads/image1_1914462a72.png"
alt="Configuring the segments"
src="Configuring the segments"
/>


## Conclusion


RFM analysis is used to evaluate customers based on recency, frequency, and monetary value. The most profitable customers are selected from the database and offered exclusive deals, while special promotions are offered to less important customers to increase their average order value. Passive customers are usually reactivated.


The main advantages of RFM analysis are increased revenue from existing customers and cost savings in marketing budgets (it's cheaper to work with existing customers than to attract new ones). However, this method is suitable for companies with a sufficiently large customer base and those whose customers make regular purchases.


Customers are evaluated on a scale from 1 to 3, where 1 is the worst score and 3 is a high score. The resulting segments are then divided into groups: the most profitable customers, less important customers, and the risk group. A strategy is developed for each group.


The analysis can be done manually (using *Excel* or *Google Sheets*) or through marketing automation tools (such as Altcraft Platform), which speed up and simplify RFM segmentation.

The main advantages of RFM analysis are increased revenue from existing customers and cost savings in marketing budgets.

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Discovering ways to better know your customers for efficient work with them?

RFM Analysis: What It Is and How to Manage It Correctly

RFM Analysis: What It Is and How to Manage It

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img="/uploads/alternativy_storonnim_fajlam_cookies_98520ab61e.png"
alt="Alternative tools to third-party cookie"
/> 

Just a few years ago, every marketer relied heavily on [**third-party data**](first-second-and-third-party-data), or cookies from external sources. However, Google's recent decision to cease collecting this information has left marketers searching for new ways to work with customer data. Although they have another year to prepare, 54% of respondents claim to have already taken steps in this direction. According to 85% of those surveyed, their work's effectiveness is directly dependent on cookies, while 77% say that Google's decision has negatively impacted marketing business processes.

## What can replace third-party cookies? 

A recent survey of marketers aimed to identify viable alternatives to third-party cookies. The results showed that the most popular alternatives were targeted social media advertising (mentioned by 55% of respondents) and direct user data collection (54%). Google Topics API was also a popular choice, mentioned by 52% of those surveyed, while 36% saw potential in contextual advertising and universal identifiers. 

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img="/uploads/Grafik_1_0974477339.png"
alt="Cookies alternatives"
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Let's take a closer look at each alternative.

## Social media targeting

Love it or hate it, social media platforms offer marketers a powerful set of tools for targeted advertising. Gen Z and millennial consumers, in particular, are highly likely to make purchases through social media, which extends the platforms' influence beyond just communication to sales.

During the period between September and November 2022, 33% of social media users made at least one purchase based on [**influencer recommendations**](what-is-influencer-marketing-how-to-build-strategy). Of these users, 28% placed and paid for their order, without ever leaving the social media site, and 22% engaged with brand representatives via the platform.

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img="/uploads/Grafik_2_a0c0cc4360.png"
alt="Generation Z survey"
/>

## Gathering first-party information

The company gathers users' personal data via its official website, surveys, email, SMS, and CRM systems. This information is utilized to develop the business and tailor offers to individual customers. Some marketers still lack knowledge of their customers' demographic information, making the use of user data especially valuable to them.

## Google Topics API

This is a service that tracks which resources users visit when searching for information on a specific query. Although it is not yet widely used, marketers are beginning to consider it a promising alternative to cookies.

How does it work? For example, if someone frequently visits sports streaming websites, Google algorithms will offer that person as an audience for sports-related ads. The user's privacy is maintained at a high level, and advertisers cannot know which specific pages the person visited. Additionally, users can edit their list of interests in the settings.

## Universal identifiers

These are unique IDs assigned to website visitors, which marketers use to distinguish between individuals on websites. They are a close alternative to third-party cookies, as they have a lot in common in the way they work. However, universal identifiers have several advantages:

* Users can be tracked simultaneously across multiple devices, improving the customer experience and eliminating the risk of data loss.
* Duplicate entries are automatically removed, resulting in a higher quality sample.
* Personal data collected through this method is suitable for targeting while ensuring privacy.

## Contextual advertising

Contextual advertising is a different approach compared to the methods described above, as it allows the placement of the right advertisement at the right time and in the right place, without relying on obtaining [**user data for targeting**](what-is-retargeting). Instead, contextual advertising takes into account the current content on the website or platform where the advertisement will be placed.

For instance, an advertisement for headphones can be displayed in the feed of a music blog, where it is relevant to the content. With contextual advertising, advertisers prioritize the context in which the advertisement will be displayed, rather than relying on past user behavior or personal data.

## Why marketing budgets are growing

After the abolition of third-party cookies, marketers had to urgently change their working strategies. On the one hand, there was an incentive to master new targeting methods, and on the other hand, there was a need to ensure the security of user data.

The requirements for preserving customer data confidentiality have become stricter worldwide. This is mainly what led to the increase in marketing budgets. 71% of marketers claim that marketing expenses have increased.

But not everything is so bad. Of course, businesses will have to allocate additional resources to find a reliable alternative to cookies and to retest and find effective ways to achieve their goals and objectives. However, those who go down this path have every chance of improving familiar business processes.

30% of marketers claim that these events have been beneficial to their business and have had a positive impact on the quality of marketing in their companies.

## Future of data privacy: what to expect

The work of marketers is becoming more difficult as they have to adapt to constantly changing rules for collecting and using personal information about customers. On a legislative level, the [**policy on data privacy is becoming stricter**](customer-data-privacy-issues-in-digital-marketing), and users themselves are questioning how their personal information is being used.

Currently, it is unclear what standards companies will have to work with in the near future. Due to this, many firms are allocating a budget for research and seeking new solutions. The first step is to find alternatives to third-party cookies. Ultimately, those who are adaptive in the dynamically evolving world will win.

Learn about the alternatives marketers are turning to instead of third-party cookies and their effects on budgets.

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For those who are already looking for ways to replace cookies.

What Alternatives to Cookies Are Marketers Exploring

5 Alternative Tools to Third-Party Cookies

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img="/uploads/audit_dannyx_014ad471e9.png"
alt="How to Maximize Your Buisness Decisions with Data Auditing"
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The world generates [**2.5 quintillion bytes**](https://explodingtopics.com/blog/big-data-stats) of information every day, with the Big Data market reaching $274 billion in 2022 and the analytics platform market following suit. This upward trend shows no signs of slowing down.

Data is highly valuable for businesses, serving as an inexhaustible source of inspiration for marketers and [**providing insights**](https://martech.org/analytics-audit-101-identify-issues-and-correct-them-to-ensure-the-integrity-of-your-data/) into consumer habits and expectations. Through data, companies can make informed predictions about their growth and select effective methods for outcompeting rivals.

However, processing large amounts of information is a challenge. Without quality analytics, the data collected is nothing more than meaningless numbers.

Data analysis, first and foremost, involves checking the authenticity of the information. But that's not all. The main task of the audit is to determine how the collected data can help the company solve the set tasks. Without accurate data, companies can't make informed decisions. Let's explore data auditing in more detail.

## How data auditing can benefit your business

Data can be a lifesaver for a company, as long as the sheer amount of it doesn't overwhelm management. Storing a large volume of information can be costly, and it needs to be organized and utilized correctly. This is where a data audit comes in. Data auditing can:

- **Consolidate disconnected databases from various departments.** Auditing, using cloud tools, can combine information across the entire company. This allows marketers to utilize valuable information collected by the sales department.
- **Resolve data integrity issues.** Company databases often contain duplicated, incomplete, or incorrect information, which cannot be used as a basis for business decisions. Relevant and reliable data is needed to solve business problems.
- **Comply with regulations.** To maintain order in business processes, many companies establish internal regulations for handling information, while some industries are regulated externally. Auditing helps ensure compliance with prescribed rules, avoiding fines for violations of data storage and usage deadlines.
- **Avoid data leaks.** [**Data leaks**](how-to-respond-to-data-breaches) occur when third parties gain unauthorized access to information that is not intended for them. Personal data security issues can be significant for companies, which is why they are vigilant in this area. Data auditing highlights vulnerable areas.

## Phases of data auditing

### Step 1. Planning

At this stage, it is necessary to develop a step-by-step plan for the audit and assign responsible individuals for its preparation, conduct, and presentation of results. The success of the entire «operation» depends on how well the planning is done.

### Step 2. Inspection and information gathering

Find out what information is currently stored in the company. How it is collected, how long it should be stored, and most importantly, how it is used in business processes.

### Step 3. Analysis and evaluation of data collected

Evaluate the situation for compliance with legislation and regulatory acts. Check data security and determine the procedure for employees' access to information.

### Step 4. Development of recommendations

Develop correct business processes and implement them in practice. Reject and replace non-working algorithms. Provide recommendations to employees on how to work with information. Remove excess data, set current storage periods for information that quickly becomes obsolete. Provide access to information for those employees who need it for their work.

<Img
img="/uploads/data_audit_steps_7cd0afe42d.png"
alt="Phases of data auditing"
/> 

## Who is in charge of data auditing?

Data scientists are usually the ones responsible for conducting data audits. In some cases, it may be appropriate to form a team of competent individuals to monitor information usage, make budget decisions, and set priorities. The team's work typically results in internal policies and data processing strategies that are then shared throughout the organization. This is how employees are trained to work with data within the company.

## Why is data audit automation needed?

Based on the analysis of events of the past three years, it can be confidently stated that customer information quickly becomes outdated: their behavior and desires change so rapidly and unexpectedly. Data quality auditing is necessary for modern businesses, as it provides the opportunity to make relatively accurate predictions of the near future. However, processing large amounts of information manually is practically impossible.

This is where data management automation platforms come in. They completely take over routine and labor-intensive processes such as cleaning the database of duplicates, monitoring information sources, and clearing the database of clutter. All of this is necessary, but it is not practical to do it manually.

Automating data auditing is beneficial for the company. It significantly frees up employees' time that they can spend on more critical and creative tasks. Moreover, the results obtained are more accurate and reliable than if the database were processed by a human. Robots do not make mistakes, do not get distracted, do not tire, and their vision does not «blur». Therefore, in this regard, they work much better than a human.

## How to choose a data audit automation tool

Platforms designed specifically for collecting and storing marketing data can help you with this task. Artificial intelligence has made significant advancements in this area, enabling precise targeting of marketing campaigns and reaching interested audiences.

Smart platforms identify new visitors using JavaScript tags and track their IDs, gradually building detailed profiles that include behavioral characteristics, demographic data, and purchase history. The data is not mixed up between users, processed accurately, and subjected to analysis.

When selecting a platform, it is important to look for the ability to [**segment users into target audiences**](how-and-why-to-segment-target-audience) in real-time. Businesses find it easier to succeed in the market when they have accumulated enough useful information. The key is to ensure that this information is relevant to improving current business processes within the company.

In this post , we will explain what a data quality audit is, how it can help your marketing, and how to conduct a data audit.

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For those who want to know what a data audit is and how to conduct it.

How to Maximize Your Buisness Decisions with Data Auditing

Data Quality Audit: How It Can Benefit Your Marketing

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Reduced push notification opt-out rates, increased OR, saved working hours

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The open rate for automatic Viber messages is around 60%, and MAU growth for the year is +19%

All user information is systematically collected in a unified customer data platform

Distributing messages across various communication channels: email, SMS, push notifications, messengers

Promo codes, bonuses, and points for building long-term customer relationships

You can create and edit any HTML template, as well as save your own custom blocks

Personalized distribution of messages to clients through communication channels

Conducting experiments and optimizing marketing strategies

Tracking the effectiveness of marketing campaigns to make informed decisions

Connecting external systems to the platform and synchronizing data with company databases

Creating questionnaires, surveys, and feedback forms of any level of complexity

The ability to communicate with your clients anytime and anywhere

Increasing user awareness of your company's offers

Customer Data Platform

Omnichannel Marketing

Loyalty programs

HTML Email Builder

Personalized mailings

A/B/n Testing

Real-time analytics

Integration and WebHooks

Forms and Surveys

Messenger Communication

Pop-Up Windows

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