If you are a marketer, you probably love data, and you definitely hate messy data. Marketing analytical software helps you unravel data in a way that saves enormous time and effort for your team.
Marketing analytics is simply the practice of measuring, managing, and analyzing data to augment marketing performance. The execution of strategies, thus built on the analysis of collected data allows marketers to be more efficient at their jobs and optimize their (ROI) Returns on investment.
Still, wondering why you need Analytics in 2021?
- Quality analytical models, No more Coding
- Strategic Investment of Time which is the real currency
- Predictive Analysis: react to changes intelligently it a pandemic or an industry-wide shift, predictive analysis keeps you ahead
- Get All your business queries answered Let your data be the answer
- Impress Leadership Better leads, Better Rol
1. Quality analytical models
Data gets recorded from fragmented and disparate sources – social media, emails, websites, surveys, sales, etc. It is not only cumbersome to gain insight through this data but this has the possibility of human error too.
Analytical models structure your data and help in segmentation.
Segmentations could be broken down into age, sex, and income, frequency of store visits, the amount spent on the products, and information on their last visit. This information could help you understand your target audience and further segment them into potential customers and most beneficial customers.
2. Strategic investment of Time
Software facilitates quick assembly and organization of your marketing activities in one place. It streamlines repetitive, time-consuming tasks. Who wouldn’t want extra time on their hands?
Automated email campaigns can move prospects further down the funnel and generate valuable leads.
Notifications can be automated around the clients’ actions. The data of a contact’s actions can be structured and if the prospect takes a noteworthy action a notification can alert your sales team to contact the leads and have a follow-up.
3. Predictive Analysis: React to changes intelligently
Predicting customer behavior, desires and planning a strategy with the help of available data is the only way to stay ahead of the consumer.
Be it a pandemic or a major fall in the market, companies need to be prepared for atrocities. With the help of Predictive analytical models, businesses can gain insights through various datasets, algorithms, and models to predict future behaviors. A company can further analyze its predictive data and use RFM methods for analyzing customer value, to understand consumer trends, and make effective decisions using the data for future behavior.
4. Get all your Business queries answered
As data needs to be extracted from multiple sources to prepare an analysis of the market situation, therefore, data can answer a multitude of questions about your business. Businesses can utilize efficient analytical models and analyze the data of their competitors in real-time. They can make changes in the prices, prepare mega offers that are better than their competitors’ sales, and can also use the competitor’s negative or positive reviews for their benefit. Analytical software assist businesses identify those layered details with which they can out-perform their competitors
5. Impress Leadership
Do you like performance awards? Do you like recognition? Do you want to bring the best deals for your company?
Well, Marketing analytical models when used efficiently do just the required tasks for you and your company. Wondering how?
- Generate better leads-With a 360-degree overview of the customer behavior such as their website visit, email responses, bounce rate, etc a customer’s LTV(LifeTime Value) can be assessed. Analytical software also helps your team to gain insights into the prospects’ interests and where they are in the purchasing lifecycle. This could help in building and implementing future strategies.
- Better ROI- Automation solutions can be tailor-made for your needs and goals. They are an investment that pays for itself quickly due to lower operating costs, reduced lead times, increased output, and more.
We have curated a list of Analytical models marketers definitely need to save those precious hours!
1. Algorithmic Attribution for Channel Contribution/ The Game Theory Model
An algorithmic attribution model uses data and automation software as a mechanism to effectively determine which ads, content, and engagements received more credit for the interaction. This process helps kick start the progression of targeted strategies that get curated based upon this analysis.
So, instead of credit being unfairly weighted toward specific pieces of content, an algorithmic model examines their journey and attempts to assign credit, based on data, to different pieces of content in the funnel.
- This method helps you pin down every channel’s proficiency over a time span and identify underperforming ones.
- It helps you evaluate the performance of each click and interaction closely.
2. Recency, Frequency, Monetary(RFM) Model
RFM segmentation model is a data-driven segmentation technique that allows marketers to segment customers into meaningful clusters based on their behavior and make tactical decisions.
But how does this information impact businesses?
The informative insights gained from the segmentations empower marketers to quickly identify beneficiary groups and target them with differentiated and personalized marketing strategies.
- Increased customer retention.
- Improved response rate.
- Elevated conversion rate
- Inflation in revenue.
3. Recommendation Model to recommend offers and products
Personalized marketing has revolutionized the way customers associate with a brand. But how do brands make these choices? Recommendation systems!
A recommendation system deals with a large volume of information based on the data recorded for a user. The data is based on an individual’s preferences and interests. This personal experience is designed specifically to ease the customer’s search and from the business perspective- Customer retention.
It finds out the match between users and various items and imputes the similarities between users and items for recommendation. Voila!
You have a loyal customer.
- A recommendation engine can bring traffic to your site
- The volume of data required to create a personal shopping experience for each customer is too cumbersome to be managed manually. Automation eases the process of structuring the data and creating varied recommendations.
- Providing reports to clients can generate insights for slow-moving products to create a drive in the pattern of marketing and sales for those products.
4. Probabilistic Clustering for Customer Segmentation
As we know crucial customer behavior records and why do companies invest in gathering data from disparate sources?
Retaining a customer’s interest for a long is a cumbersome task and has a lot of layers within it. One of the processes is to “Identify”. After a customer’s likes and dislikes are identified they’re segmented and split into clusters.
These clusters are made to divide customers in the market into relevant groups such that the customers are within similar and dissimilar groups. Clustering creates groups of persons, products, or events which can be used to determine managerial strategies, or for further analysis.
- Marketing efficiency – Softwares breaks data without using codes and a large customer base gets analyzed through a simpler structure of data. This makes the identification of your target audience quick and easy. Campaigns can be launched for the most relevant people, using the most relevant channel.
- Enrich your records: Automation software enables marketers to easily find and analyze the data for segmented customers using interactive charts and graphs without a need to create complex algorithms.
5. Cohort Analysis
Businesses function, run, prosper and evolve on great data. To curate analysis from data sources, multiple filters of segmentation are applied. Cohort analysis one such layer.
Cohort analysis is a subset of behavioral analytics. It takes its data from a larger data set, over a period of time, and instead of looking at all the users as one single unit, it segregates them into smaller related groups based on different types of attributes for analysis. Through insights from cohort analysis, companies can improve or personalize their product and strategize effectively,
- Lifetime Value Calculation
- Conversion Funnel Optimization
- Break Down of Customer Acquisition by Channel
With great data, comes great responsibilities! Interested in knowing more about our analytical model? Visit our www.rawcubes.com