Marketers and consumers both benefit from marketing analytics. The analysis helps marketers increase their ROI by identifying the best way to drive conversions or brand awareness. The use of analytics also allows for more personalized, targeted ads to be shown to consumers, which speak directly to their needs and interests.
The collected marketing data can be analyzed using different models and methods depending on the tracked KPIs. Analyzing brand awareness, for example, requires different models and data than analyzing conversions. These are some popular analytical models and methods:
- Media Mix Models: These are attribution models which look at data aggregated over time.
- Multi-Touch Attribution: Models that gather data on individuals from the entire buyer journey.
- Unified Marketing Measurement: This measurement form integrates different attribution models, including MMM and MTA, into comprehensive engagement metrics.
Why Marketing Analytics is Important
Accurate data is more critical than ever in the marketing world of today. The media that consumers engage in and ignore has become more selective.
Brands that want to attract the attention of their ideal buyers must use accurate data to develop targeted ads based not on demographic associations but personal interests. Just as a good logo design creates a lasting first impression and embodies a brand's identity, precise targeting ensures that marketing efforts resonate with the right audience. The marketing team can then serve the correct ad at the appropriate time and on the most appropriate channel to help consumers move down the sales funnel.
How Businesses Use Market Analytics
Data from marketing analytics can help your company make informed decisions about everything, including ad spending and product updates. Combining data (online and off) from different sources is crucial to get a 360-degree view of campaigns. This data can help your team gain insight into the following:
Product Intelligence
Gathering product intelligence involves examining the products and comparing them to other products on the market. Organizations can better understand the competitive advantages and differentiators in their products by speaking to customers, surveying them or involving them through surveys. The teams will then be able to better match products with the specific consumer needs and interests that drive conversions.
Customers' Trends and Preferences
Analyses can reveal a great deal about your customers. What creative / messaging resonates best with your consumers? What products do they buy, and what have they previously researched? What ads lead to conversion, and which ones are ignored?
Product Development Trends
Analyses can provide insight into the features that consumers are looking for. Marketing teams can pass this information on to the product development team for further iterations.
Customer Service
The analytics can also help identify areas where the buying journey could be improved or simplified. What are the problems your customers face? Could you simplify your product or the checkout process?
Messaging and Media
The data analysis will help marketers determine the best places to place their messages for specific consumers. The sheer number of marketing channels has made this an increasingly important issue. Marketers must know which social media and digital channels consumers prefer in addition to the traditional channels of print, broadcast and television. These fundamental questions are answered by analytics:
- What should you buy?
- What drives the highest sales?
- What is the message that resonates with your audience?
Competitiveness
What is the comparison between your marketing and that of your competitors? What can you do to close the gap, if one exists? Do you have any opportunities that your competitors may be leveraging on?
The Challenges of Data Analysis
Understanding and using the vast quantity of data available to marketers is the biggest challenge in the analysis process. Marketers must decide how best to organize data in a digestible form to gain actionable insight.
One of the most significant challenges in marketing analytics today is:
- Data Quantity: Big data was created during the digital era, which enabled marketing teams to track every click, view, and impression. However, this quantity of data will be irrelevant if the insights gained from it are not analyzed and structured to allow campaign optimization. Marketers need help with the best way to organize data to assess its meaning. Research shows that data analysts spend most of their time formatting and wrangling data rather than analyzing it.
- Data quality: This is a significant problem, not only because of the sheer volume of information organizations have to sift through but also because this data is often seen as unreliable. Forrester claims that 21 per cent of the respondents' budgets for media were wasted because data needed to be better. One dollar of every five dollars wasn't being used effectively. These dollars add up throughout an engineered to mid-more size or enterprise firm, amounting to $1.2 million and $16.5 million of budget wasted. To ensure that their employees can use accurate data to make informed decisions, organizations need to have a system to manage the quality of data.
- Data Scientists are lacking: Many companies do not have the data scientists they need. According to resurvey, only 1,9% of businesses believe they have the necessary people to leverage their marketing analytics.
- It Cannot be Easy to Select a Suitable Attribution Model: Media mix modeling, for example, and multi-touch-attribution provide entirely different insights. They offer aggregate data focused on campaigns and consumer-level data, respectively. Marketers' choice of models will determine the type of insight they get. When choosing the best model, engagement analysis can be confusing when you're analyzing so many different channels.
- Correlating Data: In the same vein, marketers must also find a way to normalize data to make them comparable. Comparing online and offline interactions is difficult since they're typically assessed using different attribution methods. It is here that unified measurement platforms and marketing analytics can be of great value by organizing disparate data sources.
Want More Information About Our Services? Talk to Our Consultants!
Marketing Analytics Software is Used for What?
The marketing analytics software overcomes these challenges by quickly collecting, correlating, and organizing valuable data. That allows marketers to optimize their campaigns in real time.
The speed with which modern marketing platforms can process and store massive data sets is a valuable asset. The fact that so much information is available can be a disadvantage for marketers, as they cannot parse it in time to optimize real-time. The processing power of analytics platforms allows marketers to make changes before the end of the campaign, increasing ROI.
Many platforms, including Marketing Evolution, use unified marketing measurements to aggregate and normalize marketing data across different channels and campaigns. That simplifies analysis.
Advanced analytics platforms offer insight into the brand equity of a company and what specific segments of an audience think about creative elements. It helps them determine the ROI of brand building and how they can personalize experiences.
Features and Capabilities of Marketing Analytics Software
Consider these features and capabilities when implementing marketing analytics solutions:
- Analyses and insights in real-time.
- Brand measurement capabilities.
- Individualized data at the person-level.
- It is possible to correlate online and offline attribution metrics.
- Contextualized customer and market insights.
- Media Plan for the Year: Recommendations.
Implementing Marketing Analytics Into Your Program
- Create a Plan: Establish goals for your reporting and benchmarks to ensure accuracy and effectiveness.
- Implement your Plan: Concentrate on getting your data into the right places within your company where it will be most beneficial. The right team is essential.
- Optimize your Plan: Once your plan is implemented, you can adjust your data or team based on the results to help move more leads through your sales funnel.
Market Analytics Managers Need Specific Skills
When marketing teams are looking to hire analytics managers to help them conduct better analyses that will lead to profitable and engaging campaigns.
It is essential to focus on hiring those who:
- Perform Quality Analyses: An analytics manager should have extensive experience analyzing large datasets to gain insights, such as buying patterns or engagement trends among the targeted audience.
- It is Essential: To make optimization recommendations based on data trends. Once you have gained insights, it's crucial to be able to provide suggestions to improve campaigns that are underperforming. Data may reveal that consumers only engage with brand content in the evening. That could inform a shift in strategy to place the ad during the commute home rather than in the morning.
- Understanding Consumer and MarTech: Analytics managers must be aware of the latest consumer and MarTech developments. Understanding the consumer's demand for an omnichannel, seamless experience and how consumers engage with virtual and augmented reality is crucial to determining optimization opportunities.
- Analytics Tools: The next step is to familiarize yourself with the various analytics tools, such as automation platforms and tools for analyzing data. These tools are vital in helping you reduce the time between consumer engagement and consumer insight.
- Collaboration with Stakeholders: Members of the analytics department must also be able to use their data to create a compelling narrative for the stakeholder and show how other departments, such as product development or sales, can utilize these findings to increase engagement.
What are the Steps in Marketing Analytics?
Here are the four key steps to improve your analytics program:
-
Understanding What you Want to Measure
You can track many different aspects of a campaign: lead generation, conversion rate, and brand awareness, to mention a few. When you begin to analyze data, you should first identify the problem or insight you're trying to gain.
-
Set a Benchmark
How would you describe a successful marketing campaign? The type of metrics and data that marketers will collect depends on this. If the objective is to raise brand awareness, the benchmark for success might be a higher percentage of loyalty shown in the customer experiences panel rather than a click or impression online.
-
You can Assess your Current Capabilities
What are you doing? What are your weak points? Understanding your weak spots can help strengthen programs, whether you are assessing the results of offline campaigns or identifying the media that converts best.
-
Use a Marketing Analytics Tool
Marketing analytics will become increasingly important as consumers are more discriminating and data sets grow. A platform like our Marketing Measurement and Optimization Platform uses unified measurement for marketing to identify messages and media types that work best. It gives a real-time, holistic picture of the campaigns performing well and those not.
The IoT and AI Capabilities of Salesforce are Helping it
Salesforce is a customer relationship management software that allows you to solve problems by responding to questions. That based customer relationship management platform helps build better relationships and ensures that your customers' needs are met. It is a cloud-based software that works like a SaaS, which means it's very convenient because there is no need to install it.
Customer service is always a priority in business. AI can be used to improve customer service. Artificial intelligence is now a standard technology that we use in daily life. Meanwhile, artificial intelligence (AI) is increasingly used by organizations to enhance their brands reputations and increase customer loyalty.
Artificial Intelligence is revolutionizing the way that businesses interact with customers. AI is beneficial to all businesses, large, medium, and trim. Salesforce has been quick to incorporate AI into its systems. Salesforce has given its customer service agents the power to utilize Einstein, Salesforce's AI for CRM.
The first internet of things (IOT) that incorporates Artificial Intelligence in its processes is Salesforce. The Salesforce AI allows every employee to be more productive and provide better customer service. Businesses can use AI to plan and predict future actions, analyze data and make quick and intelligent business decisions.
Einstein's new capabilities enable businesses to become intelligent services. Einstein AI can enhance your business processes and improve customer service. Salesforce AI helps modernize corporate processes.
What is Salesforce Einstein?
Salesforce Einstein is the first CRM platform to incorporate artificial intelligence technology. Einstein was explicitly designed for platforms that support customer success. The AI technology integrated into the platform allows users to automate and develop quickly.
Intelligent case classification allows users to perform more work in less time. It identifies and recommends the next steps. Einstein's platform uses intelligent bots to answer customer questions so that agents can handle complex cases. AI also helps agents by sending them replies and making suggestions. The platform has been data-ready, so no prior preparation is required.
Salesforce Einstein can also automate processes, learn about customers, predict outcomes, and make recommendations. Salesforce Einstein uses automated machine learning (AutoML) to accomplish this. AutoML analyzes your data to find errors. It also helps to understand why your customers purchase and to predict what they are most likely to buy. Admins and developers can also use the Einstein feature to build AI-powered applications and integrate them into workflows.
Salesforce Einstein for Sales, Marketing, and Service
Salesforce Einstein, an artificial intelligence system, provides users with the most comprehensive and current view of their customers and prospects. AI is primarily used in Salesforce CRM to provide a personalized experience for customers. The AI automates repetitive tasks to improve the results of the following actions.
Salesforce Einstein AI-powered apps can be created quickly by integrating refined AI capabilities in your platform, service, marketing, platform and app:
- You can easily make predictions and suggestions with just a couple of clicks.
- AI should be integrated into all business processes and workflows.
- Any app or recording can benefit from predictive insights.
- Give your team or sales team representative relevant information.
1. For Sales
The Salesforce Einstein AI can predict results and help boost your business. It will also reveal the differences between closed deals and those that are not. That helps determine whether buyers will be less likely to buy a particular product. The software then pinpoints the best leads and offers suggestions on converting them.
Business owners can use Einstein's Predictive intelligence to predict the sale of their company in the future. Accurate projections help managers and CEOs create effective strategies. Einstein Prediction Builder can be used to create tailored forecasts of what your business will do next. Salesforce lets users create AI models without having to use complex codes.
Key Benefits:
- Einstein Sales Cloud helps you close more deals. Focus on sales while Einstein takes care of the rest.
- Scores can be built based on sales data to identify the leads and opportunities likely to convert. Then, prioritize your next steps accordingly.
- To ensure your reps take advantage of significant business growth opportunities, examine your activities to engage with prospects and external news.
- You can save time entering data by automating contact generation and synchronizing your email calendar with SF.
- Discover historical sales data, trends in business, the whitespace and other insights to increase your sales.
- Automatic machine learning can predict your weather and give you intuitive insights.
2. Service
Salesforce Einstein AI helps you better understand how your customers feel about your product or service. Service Cloud Einstein allows contact centers to discover real-time customer insights powered by AI (artificial intelligence) and ML.
That helps improve customer service for businesses. That leads to a positive client experience and an increase in customer retention. The use of natural language processing can extract patterns from the spoken word that will help answer questions, identify conversations related to brands, or respond to requests.
Key Benefits:
- Turn your department of customer service into a powerful growth engine.
- You can help your customers find answers instantly by using customized chatbots. That will reduce the time they spend on hold.
- By allowing agents to spend time with their customers, they can save more of their own time.
- Intelligent conversation suggestions and in-context content recommendations can help improve customer satisfaction.
- Dashboards pre-built provide insight into workers' activity, cases, chatbot performance and much more.
3. Marketing
You can create customer journeys by combining Einstein with Salesforce Marketing Cloud and running email campaigns. Einstein will help with every aspect of your email marketing. Einstein AI can also provide helpful insight into the timing and content of your marketing campaigns. Salesforce Einstein has the most robust AI in CRM. That ensures you communicate consistently in all of your marketing channels.
Key Benefits:
- You can better understand your customers with the Einstein Prediction Builder insights. These are derived from marketing interactions, brand interactions, and social media conversations.
- Give your marketing team the tools to ensure every interaction with customers and customer journey succeeds.
- Use recommendations to engage more effectively with customers. These include when to contact them, how they should be contacted, and what items you need to focus on.
- Personalize your content and messaging based on customer preferences and intentions.
Salesforce Artificial Intelligence
Salesforce Einstein AI incorporates three major segments:
1. Machine Learning
Machine learning uses historical data and statistical models to predict future business outcomes and customer behavior:
Einstein Prediction Builder: With Einstein Predictive Intelligence, you can enhance your decision-making and allow users to build robust prediction models within a codeless setting. You can use the results of this technology to enhance and improve your business.
Einstein Next Button Action: Einstein's Next Best Action allows you to deliver accurate recommendations at the appropriate time. The display functions and offers can be modified anytime to suit your needs.
Einstein Discovery: Salesforce Einstein Discovery uses artificial intelligence to provide embedded models of data analysis. AI-powered analysis reveals patterns and trends in your data, allowing you to make forecasts or suggestions. You can act with confidence and overcome complicated issues using Einstein's Discovery.
2. Natural Language Processing
Natural Language: Processing can be used to engage your customers better. Natural Language Processing (NLP) identifies linguistic patterns to respond to questions, track conversations online about your brand, and answer requests.
Einstein Language: Einstein's Language provides accurate results when analyzing customer feedback or text-based interactions.
Einstein Bots: Einstein Chatbots are primarily designed to handle client requests and improve customer satisfaction through timely responses. Virtual assistance like Einstein Bots from Salesforce's artificial intelligence is the most effective way to engage consumers 24/7 and improve their experience.
3. Computer Vision
Einstein's features are based on computer vision. That involves pattern recognition, data processing, and different uses. Computer vision and data processing can be used to track products, identify text on photos, and more.
Einstein Vision: You can easily track the brand you are promoting on Facebook and other social networks. You can create pre-trained or custom classifiers to handle different use cases.
Want More Information About Our Services? Talk to Our Consultants!
Conclusion
A successful AI marketing campaign is dependent on having the correct marketing analytics in place. Understanding where and how your audience engages and what drives sales will help you to improve your ROI.
