Data Analysis Using MS Excel for Sales & Marketing Professionals
Microsoft Excel is widely used by businesses for regular data analysis. Excel features like charts, pivot tables, what-if analysis, conditional formatting, sorts & filters are popularly utilized by data analysts.
Modern-day businesses tend to utilize various tools and platforms for advertising, sales, customer support which create individual data silos. Decision-makers, thus need to consolidate all of these data silos at one place and then analyze the data for a complete understanding of the business across various verticals. However, most of these tools are not inter-compatible, most offering CSV or Excel file export options. Thus various sheets need to be downloaded from each software and consolidated in Excel for data analysis. In this article, we will see just how effective Excel is for comprehensive data analysis.
How does Data Analysis help in Sales & Marketing?
Data Analysis will provide deeper insights which are used to optimize marketing campaigns. Be it their budget allocations, target audience, geography. Let us cite an instance: a marketing manager is planning for a paid campaign on Google. He will definitely have a basic idea of the number of clicks the ad will generate within a particular timeframe, based on keyword trends, bounce rates on the landing page and the number of leads that will be generated from those clicks. A comprehensive data analysis will reveal the expected revenue/profits that will be generated from this campaign, enabling him to easily calculate ROI and optimize the marketing budgets accordingly and set benchmarks for each campaign.
How to Perform Data Analysis in Excel?
Let us list down the popular features & functions of Microsoft Excel which are commonly used by business professionals for data analysis in Excel.
Pivot tables allow you to obtain relevant data from a large dataset. This is considered to be the most useful technique of analysing data. You can insert a Pivot Table, then drag fields, Sort, Filter or Change Summary Calculation. you can also make Two-dimensional Pivot Table. There are useful functions like Group Pivot Table Items, Multi-level Pivot Table, Frequency Distribution, Pivot Chart, Slicers, Update Pivot Table, Calculated Field/Item and GetPivotData. Know how to create Pivot Tables.
What-if Analysis helps to experiment with different scenarios for values or formula. Start by clicking on What-if analysis function in Excel. After putting in details for the required scenario, click the summary button. You can also explore Data Tables, Quadratic Equation and Goal Seek under this function. To understand this feature better, click here.
The Conditional Formatting feature allows highlighting cells with a distinct colour, depending on the value assigned to it. Conditional formatting is beneficial for managing rules, data bars, colour scales, icon Sets, find duplicates, shade alternate rows, compare two lists, conflicting rules, checklist and create Heat Maps. Learn more about this function.
Creating Charts is quite easy and depicts data in various ways which is more useful than a sheet. You can create a chart, change the chart type, switch row or column, legend position and data labels. The different types of charts available in Microsoft Excel can be Column Chart, Line Chart, Pie Chart, Bar Chart, Area Chart, Scatter Plot, Data Series, Axes, Chart Sheet Trendline, Error Bars, Sparklines, Combination Chart, Gauge Chart, Thermometer Chart, Gantt Chart and Pareto Chart. Know how to create charts.
Sort & Filter
The most common functions used in Excel are Sort and Filter. Sorting can be done in ascending or descending order within columns. Sorting can be done by colour, reverse or randomize List. Filters are applied to display data which meet certain criteria. There can be Number and Text Filters, Date Filters, Advanced Filter, Data Form, Remove Duplicates, Outlining Data and Subtotal.
Vlookup & Hlookup
Vlookup & Hlookup are very important functions used by analysts to find a value in a database and fetch other values corresponding to it. It is commonly used by data analysts to connect and consolidate meaningful data from different excel sheets. For a better understanding of these functions, click here.
Verdict: Excel is the best support tool for Standard Data Analysis.
Excel can be used to extract data from various sources like websites, sales platforms and other excel worksheets and workbooks. while performing a complex data analysis, data is consolidated to narrate the detailed business performance, whether the desired goals are being achieved or not. Excel is useful in importing, sorting and organising data, as well as creating reports for historical analysis. The importance of historical analysis is to determine whether a business has met its objectives. When financial plans are given a unified view, a historical view will allow the business to deal with unprecedented incidents and replicate the good ones.
Also read about How Sales & Marketing Team Use Google Sheets for Data Analysis?
Can Excel be Used for Complex Data Analysis?
Excel has the capability to do predictive analytics using plugins. For complex data analysis, the add-ons in Excel will centralise all your complex business formulas and calculations from multiple systems in one sheet, view, or graph. Having all your data in one centralised place and detailed, customisable dashboards enable you to easily compare, measure, and analyse complex data so that you can make informed business decisions.
A company may sell its products and services in multiple countries. It uses eCommerce platforms for their Online Stores. They have different marketing platforms, payment gateways, inventories, logistic channels and target audience in each country. Hence, businesses are bound to use several tools and applications for each job to be done.
For a simple calculation of profit, where
Profits/Losses = Sales – Expenses
The sales data will come from eCommerce sites, Expenses from the marketing costs in the platforms like Google Adwords, Facebook Ads. There can be other expenses like purchasing stock which might come from inventory management platforms like Olabi, which further need to be added to all other expenses occurred that is usually present in accounting software like Freshbooks. Additionally, there will be different data silos for each country. Thus, you have to pull all of these data from multiple platforms for each country separately in Excel, and then analyze all of this data together with the expense data and calculate profits. It involves a lot of working hours which costs money, and there is usually a time lag involved, which reduces the accuracy of the analysis and its effectiveness as the data is not analyzed in real-time. Thus it becomes necessary to consolidate all of the data in a data warehouse using a data pipeline.
Daton is a modern cloud data pipeline designed to replicate data to a cloud data warehouse with the utmost ease. Daton is the cheapest data pipeline in the market which has built-in support for more than 100 applications, databases, files, cloud storages, analytics, CRM, Customer support, and many others. Analysts can replicate data from any source to any destination (BigQuery, Snowflake, Redshift), without writing a single line of code and in a matter of minutes.