ECommerce Product Data Enrichment converts search to sales

by contentProduct Data EnrichmentblogProduct Data Management

Product data enrichment is about providing vital information of your product accurately. Customers visit your website but within no time they leave the site, as the data available of your product doesn’t keep them engage. The information of your product may not be relevant and satisfactory as per customers need. This is here, the product data enrichment plays its role and helps the online owners to convert visitors into customers.

If a visitor is not satisfied with your product data information, he will be inclined towards your competitors product-where he will get all information about what is he looking for and buy from them.

What is Product Data Enrichment ?

Product data enrichment is all about finding gaps and inconsistencies in product data and rectify them with up-to-date, accurate and relevant information. Product enrichment helps in providing more details of your product like, color, size, descriptions, measurements attributes etc.,

Most eCommerce owners tend to lose customers even before the checkout, as the customer find the product description- inadequate or inappropriate. Customers making online purchases are solely dependent on product data that store owners publish on product pages. Online merchants operating on leading eCommerce platforms including Magento, Zen-cart, X-cart etc., and market places such as Amazon, eBay etc., have successfully reduced the bounce rate on their online stores with the help of product data enrichment service.

How do eCommerce Product Data Enrichment increases sales

#1. Reduce cart abandonment :

shopping cart abandonment is one of the biggest problems online business owners are facing. Cart abandonment refers to that customer behavior, where in a customer browse a website and after satisfaction, adds these products to his shopping cart – but he will never purchase it. 2 out of 3 customers leave the website before using credit card. This usually happens when customers are not satisfied with the product information provided by the website. Wavering thought process makes customer to abandon cart at the last moment. Adding valuable and appropriate product details also enrich their product with required content ensures that customers don’t abandon carts due to wavering thoughts.

#2. Contextual information :

in simple words contextual information means, extra information that helps you understand something better.

Consumer product companies, mostly provide inadequate product information, which doesn’t have the power to compel the customers for repeat visits. The contextual product description will help the customer to understand that by purchasing your product, it will add value to their lives. They can enjoy a higher standard of life. This makes it necessary to write the product copy in a manner where it provides all the contextual product information required. Contextual marketers deliver the most relevant information possible to consumers in the timely manner possible. Contextual information helps you gain greater intimacy with customers and target market segments more efficiently.  

#3. Product page meta data to boost traffic :

There are a lot of things that go into optimizing a website for search engine optimization. One of these is metadata. The primary job of metadata is to tell not only the visitors but also the search engines what a page is about. Customers face difficulties in finding what they want to buy. It is due to inefficient product title description and mega tags. In such cases product metadata needs to be fixed immediately. Title tags and meta descriptions are essential as

  • It is the consumers first digital touchpoint
  • Gets you more clicks.
  • It helps with better rankings.

With better ranking rate and more clicks will ultimately boost the traffic to your website, thus accelerating sales.

#4. Product categorization :

it is clear that if information is not clear, properly categorized, website visitors will rarely continue to try and find the products they want. You cannot avoid product categorization. It is the ground level work. Product categorization helps the user to complete the buying cycle, before leaving the site. If product categorization is not carried out properly, it will result in lost sales. Managing product data is all about validating and fixing errors in product categorization. Developing product taxonomies, fix product descriptions with appropriate keywords and tags and classifying it correctly will help you boost your e-commerce sales.

#5. Cross-selling and up-selling premium products :

up selling is the practice of encouraging customers to purchase a comparable higher-end product than one asked in query. While cross-selling invites customers to buy related or complementary items. Both up selling and cross selling if done properly, provides maximum value to customer and increases revenue without any extra cost. Product data enrichment ensures that, purchaser visiting your website end up finding related or alternative products along with the ones they were looking for and also in the absence of it. This can be fulfilled by exhibiting all related products on a particular page.

#6. Helps in making purchase decision :

Product data enrichment facilitates faster product discovery, accurate search results and rich experience. It ensures the customers visiting your site have no doubts in mind when it comes to making a decision to purchase. Product enrichment helps customer to find right product faster with proper product description and making the buying process smooth and simple – will leave customer with no confusion and end up the cycle by buying the product and with a good customer experience.

#7. Enhances consumer experience :

With answer to all queries that customers have in mind- data enrichment  facilitates online retailers to enhance consumer experience. When customers purchase products that add value to their lives make a big difference to your overall sales number.

Conclusion :

Product data enrichment aims at enriching your product related data with relevant information. It analyzes your data thoroughly, inconsistencies, errors and duplicate entries and enrich product descriptions with keywords to improve the search rankings of your products. Implementing product data enrichment helps you increase your e-commerce sales figures and converts product search to product sales.

Tips for prioritizing Product Content Enrichment

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People are adopting online shopping like never before and the global eCommerce market size is expected to grow very high in the coming years. Current technology trends and accessibility have made it possible for every business to create an eCommerce platform in no time and start selling. As the demand for online shopping is increasing, it is necessary to see that your eCommerce site is functioning well enough.

Sometimes you may find that the content on your product page is inconsistent, lacks certain important information or deficiency in description. The product content is not optimizing your search engine ranking. Comparatively, with other websites, you may find that your website is lacking visual content, multiple product images etc. This is the time that you have to improve the quality of your online product content. If you are dealing with hundreds of SKUs (stock keeping units), the resources at your disposal may not be sufficient to enrich product data for every single product you a stock or sell.

Here are the ways to prioritize your product data enrichment for a better search engine ranking and enhancing your customers experience –

#1. 80/20 rule :

It is also known as “Pareto’s principle”. This rule is applicable in many fields like sports, health care, business etc. Applying it to the business world, the 80/20 rule suggests that 80% of your company sales come from 20% of your customers. In the sales and marketing sector, you can use the 80/20 rule as a guide with highly effective business solutions.

The main question, that most of the businesses are facing is – how to identify 20% of their main customers in order to make a marketing strategy, to achieve 80% of the result with minimum cost. Your online site may have hundreds or thousands of contacts on your product list. Keep a close watch on which customers made a recent purchase, who are frequent buyers, which ones were generous in their purchases. Which products are more in demand? Identify the 20% of your products that drive 80% of your eCommerce sales and make these products fully enriched.

#2. Customer focus:

It is important to know, which user behaviour will end up the cycle by purchasing your product. Many eCommerce websites are launched only for such selected customers who are generous in their purchase. In such cases, you can enrich those products content that is more applicable to those customers. By focusing on the products they purchase, you can increase the value of your eCommerce site and also enhance user experience.

#3. Enrich core product :

The essential products which define your brand and provide value to your eCommerce website, such products are best to choose and enrich the product content of those core products.

#4. New product launches :

When you are planning to launch a new product, make sure that your new product has relevant, current and high-quality content. Enrich your new product page with engaging and informative content, because in digital marketing ‘content is the king’.

#5. Seasonal products :

If your Online store is dealing with seasonal products, make sure that during that season your product page should be enriched with the rich and current content of the product. Since it’s a seasonal product, every season new feature will be added to your product. For example ceiling fans, refrigerators. These products are out every season with some newly added feature. Make sure you highlight your product page with that particular feature.

#6. Gated eCommerce :

If your website is accessible by only restricted or selected customers than those product pages should be enriched with relevant and informative content. For instance, I-phones are launched every year with some newly added version, so your content must have that current feature of your product, so that you can target the gated customers.

#7. Gap filling :

Identify weak points of your product content. Sometimes your product content has an insufficient description of your product then you have to enrich your content with relevant and current information about your product. For instance, if your product page is without images than you can add product images and can make it more engaging.

Pull up:

Prioritizing product content aims to boost more sales and increased revenue. Google analytic helps in identifying products with low online conversion rates. Once you identify the products – you can enrich the content of those products, helping you to increase conversion. Similarly, PIM also helps you in improving the quality and consistency of your product page, by enriching the content with more images, visual information and current eye-catching trends. Engaging content will attract buyers and convert them into online sales.

Which solution is best for managing your Product Data?

by eCommerceblogProduct Data Management

The growth and long term success of e-commerce business depends on effective product data management solutions. Product data management [PDM] is a structural approach to help control, access and share large quantities of crucial data, that provides significant value to your business. It is important and tedious to maintain and manage this product information.

What is data management ?

Data management is the process of collecting, storing and using data efficiently, securely and cost effectively. The main objective of PDM is to collect and connect data from different sources and use it efficiently during decision making.

Effective data management is a combination of best practices, processes procedures with the help of effective tools, to control and manage data resources effectively. Today’s data managers need a system that is versatile enough to meet all of their employees access needs with full data security.

Tools for effective data management:

#1. Users tracking and analysis tools :

Customers surveys or interviews are valuable tools in their own way. They will tell you only what your customers say and think and help you analyze what those customers actually do

      But the users tracking and analysis tool  intelligently  gives you an insight- how your user or your website visitors are actually engaging with your product and your content. Tools like pendo or amplitude can uncover important realities about what is good or inspires your users and what is not. Companies like Accenture use Amplitude and companies like Atricem use the Pendo tool.

#2. Road mapping software tool :

Road mapping tool is used for project planning, progress tracking, activity coordination and team collaboration. This software is a must on any list of product management tools. Best example for a road mapping software tool is product plan. 

 Product plan is a great tool for managers who prefer to create an overview of their project. It allows us to create road maps quickly and easily. It communicates plans for future work to the team and builds up a consistent and standardized work process. 

#3. Customers survey tools :

Through customers survey tools  you can send the survey out to your customers and easily track and analyze the results. For gathering quick answers to important user questions, these tools are extremely helpful.

Web based survey tools like survey monkey or type form, have so many types of pre-formatted questions that whether you want to offer multiple-choice questions, drop down lists or just open comment fields. These tools help you to do surveys in minutes. Even emails, online surveys are easy, convenient and inexpensive that it can be tempting but they may upset your UX if not executed properly.

#4. Recording app for customer’s interview:

When you speak on the phone with customers for some queries, it’s always a great idea to record a call. You never know when a customer will offer a valuable insight like why they are using your product. Tools like, Go To meeting or zoom makes it easy to record those conversations and references them later.

Go To meeting provides a fast, easy and reliable professional online meeting solution that enables customers to meet face to face and communicate virtually. Zoom brings teams together in a frictionless and secure video environment. This platform provides video meetings, webinars on all devices. 

#5. Team messaging tools :

With the outbreak of pandemic, a lot of people started working from home. These business messaging tools helped teams to stay in touch and stay productive even if they can’t come to the office. When your product data starts giving trouble or gets underway, you will want an easy and immediate means of communicating. There are certain tools that allow easy and centralized team communication. Slack and confluence are best examples for team messaging tools.

Slack is a channel based messaging platform. With slack, people can work together more effectively, connect all their software tools and services and find the information that they need to do their best work-all within a secure environment. Confluence is a collaboration tool that helps teams to collaborate and share knowledge, data efficiently.

#6. Flow charting tools :

Creating a customer journey map is helpful in giving your company a clear view of your customers full experience with your company. Journey maps focus specifically on UX of using your product. It shows all the main points of the visitors, from the first visit to your website till purchasing and using your product.

Flowcharting tools like Microsoft Visio and OmniGraffle help in mapping out the specific aspects of users’ experience with your product. These flow-chart tools help you to uncover insight and helps you to develop your future strategies through your product road map.

#7. Product management tools :

Product management tools help to simplify the tracking and documenting details. For example Trello is visually appealing and is known for its fun use of cards, lists and boards. Trello is easy, free and flexible to manage your projects in an organized way. Gantt chart is an open source software that allows its users to identify problem areas in the workflow. Another PM tool is Jira. It’s excellent for team collaboration. This tool helps you track and monitor the entire product creation process. Jira can be used on a server, a data center platform and in the cloud. Other popular project management tools include Microsoft project, Pivotal Tracker will help you to execute on your road map and keep your backlog organized.

#8. Presentation software tools :

Tools like PowerPoint, Google slides and keynote can all be used to create presentations. You can make your presentation more engaging by including images, creating movements. Vision decks for example can be a powerful way of communicating your product vision to your clients/customers and earning their buying decision.

Conclusion :

While there are many tools out there to handle different products management requirements, the choice of product management tool/software depends upon your specific needs. Product management users must use the right product management tools and product management software. There are many product management tools and product management software that you can try first, before moving on to paid ones.

Steps for Data Cleaning and why it matters?

by contenteCommerceblog

In the world of data processing there is one saying

   “Garbage in – Garbage out”

It means your results are only as good as the data you’re using to get them. Incorrect or inconsistent data leads to false conclusions and false conclusions have bad impact on your business. This is true if you are a researcher, small business owner or a large enterprise.

If you make your decisions based on incorrect or inconsistent data, you can be sure that the business results will not be good. You may lose clients, business opportunities, time and money.

Data cleansing is referred to as data cleaning or data scrubbing. Data cleaning are steps to clean data before using data for analysis. This is accomplished by removing or modifying data that is incomplete, incorrect, irrelevant, duplicated or inaccurate. This technique minimizes the risk of wrong or inaccurate conclusions or results.

Steps for cleansing data :

The techniques used for data cleaning may vary according to the types of data your company stores.

Following are the basic steps for cleaning data :

#1. Removing duplicate or irrelevant data :

Duplicate observations will happen most often during data collection. When you combine data sets from multiple places or receive data from clients or multiple departments, there are chances of creating duplicate data. Deduplication of data has to be considered in this process.

Irrelevant data are those observations that do not fit into the specific problem you are trying to analyze. For example if you are analyzing data regarding young customers, but your data set includes older generations, then in such case you have to remove those irrelevant observations. This can make analysis more efficient.

#2. Structural errors : 

There are different types of structural errors from typos to inconsistent capitalization. This can create problems when categorizing or grouping data, so they need cleansing. For example “gender” is a categorical variable, usually of two classes, male and female, but you may encounter more than two different categories of the variable such as : *m; *male; *F; *fem. Data cleansing helps to recognize such mislabeled or inconsistently capitalized classes. Also review you data collection and data transformation process to prevent data issues.

#3. Handling missing data :

‘Missing data’ is a tricky issue. Just be clear that you cannot simply ignore missing values in your data set. Deciding whether to drop, impute or flag missing data. Using/not using the missing data affects the accuracy of your analysis.

  • Imputing : It means working out the missing value based on the other data. The pattern will be re-created that the observations have already created.
  • Dropping : Dropping observations that have missing values when analyzing statistical data. Study shows dropping is better than imputing values.
  • Flagging : Flagging means telling your ML algorithm about any missing value. Flagging is done when the data is missing continuously, rather than randomly.

#4. Filtering outliers :

Another thing you have to remember during  the process of data cleansing are outliers. Outliers are values that are totally very different. For example, you are researching your app user’s age and find entries like 72 and 2. The former might be a senior citizen who is up to date with the technology. But the latter is mostly likely an error since toddlers don’t use apps. If an outlier proves to be irrelevant for analysis or proves to be a mistake, it should be removed, in doing so you can increase the performance of the dataset.

#5. Standardization of data :

Cleansing your data includes standardizing it, to have a uniform format for each value. For example, all values of height should be in the same unit, so you may need to convert from feet to meters or vice-versa, to achieve uniformity. 

Make sure that you use a standardized unit of measurement. These include weight, distance and temperature. As for dates, choose either the USA style or the European format.

#6. Validate the data :

In the conclusion of the data cleaning process, you should be able to answer these questions:

  • Does the data make sense?
  • Is the data is appropriate with regard to its field?
  • Does your data help to develop your next theory?

False results, as a result of incorrect data, may inform poor strategy and decision making. Conversely, data cleansing can help achieve a long list of benefits which may lead to maximize profits.

Pull up :

Monitoring errors and better reporting to see where errors are coming from, Making it easier to fix incorrect or corrupt data for future applications. Clean data helps in taking effective and efficient decisions, resulting in increased productivity and revenue. Using tools for cleansing will make for more efficient business practices and quicker decision-making. Therefore cleansing data from time to time is advisable, for a good result.

Top Three Reasons to Normalize Your Data

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Data Normalization

Most businesses focus on data cleanliness. Having accurate data helps to segment customers and analyze the data in terms of marketing in order to engage the brand further. There are a number of reasons to normalize your Data. This facilitates the entire data cleaning process and keeps the customer data clean and organized. Without data normalization one may face several types of data errors.

Data normalization is the process of restructuring the data to ‘normal’ in terms of data integrity. It is a key part of data management that can improve data cleansing, lead routine, segmentation, and other data quality processes.

Data normalization makes the data look clean, organized, easy to read and navigate through, and uniform across the entire customer database. Normalization includes standardization of specific fields in the customer database which brings uniformity.

In addition, here are the top three reasons to normalize your data.

1. Identifying Duplication of Data

Data duplication is a crucial problem that companies face and getting rid of duplicates is an important part of data management. Data duplication can hinder the overall customer experience. Customers may receive the same data more than once which is not very appealing. It not only impacts the sales and marketing aspects of the business, but also increases data storage cost. Normalization makes it easier to locate and eliminate the duplicated data.

2. Improving Lead Scoring

Lead scoring is defined as the process of assigning a value to specific leads in the CRM so that you can identify and grasp potential opportunities. Effective lead scoring is dependent on high-quality data and effective segmentation. For example, a B2B company will assign value to its specific leads based on the job titles as a variable. Moreover, proper segmentation is not possible without normalization. This will impact the values and business might lose out on the best opportunities. Data normalization enhances data quality and improves the process of lead scoring.

3. Reduce Response Times through Normalization

In B2C companies, customers expect faster response time for their queries. Having to feed in thousands of names along with their responses can often be time-consuming. In order to achieve an organized data, companies must have a perfect internal administration team and must use the data normalization tools. Data normalization ensures reduced response times and well-structured data.

There are specific tools that can identify standardization issues and assist in the data normalization process. And also these tools analyze the existing customer data to generate an assessment report. So, based on the report, multiple categories are assigned to help companies normalize and standardize their customer data. This is an ongoing process, which means that the business can track and fix the standardization issues as they arise. In addition, the number of data normalization errors can be limited, resulting in a high-quality customer database.

Data Normalization and Its Importance

All about Data Normalization and Its Importance

by with No Comment contentProduct Data Enrichmentblog

Advancement in technology and the changing work pattern within organizations has led to an increased importance for data management. Companies are building databases that are helping them collect, store, and analyze information. When it comes to bid data, another term that is widely used is data normalization. In this blog, we will understand more about data normalization and its importance.

Data normalization can be defined as the process in which data is organized in a way through which data users can easily analyze the data further. Data normalization has several applications. For instance, data normalization helps to get rid of any duplicate data. This reduces any possible redundancies which can adversely affect the data and enhances the capability of efficient data analysis.

Data normalization also helps to group the data together. The data that relates to each other is clubbed together into a single group making it easy to view the entire data at once. Sometimes the datasets have conflicting information. Data normalization helps to resolve all the data conflicts before any further analysis. By using the data normalization process, one can convert the entire data into a specific format which is simpler to read and analyze.

Now that we know about the applications of data normalization, it is time to understand the importance of data normalization.

A well-functioning database must go through the data normalization process. By why you ask? As discussed earlier, data normalization helps to get rid of all types of data defects and makes it easier for the users to analyze the data. Since the defects can occur at all times while the data is modified or updated, data normalization must be carried out regularly.

If a company does not use the data normalization process, then although the company would gather data, most of the data would be unorganized and unused. The data would take up most of the space and will not be of any benefit to the company. And since there is a lot of money invested in data collection and database designing, unused or misused data can lead to serious financial losses.

In addition to rectifying any data anomalies and faster analysis, data normalization offers several benefits to the organization:

  • Databases take up less space – Although technology advancement gives bigger data storage options, data normalization offers ways in which lesser disk space can be used for storage.

  • Enhance performance – Databases that are not unnecessarily loaded can lead to faster data analysis and increased performance.

  • Faster data upgradation and modification – Since the data anomalies are rectified, data can be easily updated and modified.

  • Data can be used to improve an organization’s performance – Company can look at the data to understand the company’s performances in different departments.

  • Can be used as a business intelligence tool – Data normalization can easily cross-examine the data coming from various sources.

Data normalization process works wonders for data scientists, business analysts, and people involved in database maintenance. It is considered to be one of the most necessary processes to be carried out by every company that deals with large data collection, storage and analysis.