With data currently being generated at a rate never seen before, the need for more efficient ways to manage and process it is more crucial. Data automation is a must for businesses wishing to keep up with their competition and remain relevant to customers.
Luckily today, there are many different ways to do this.
As most company departments have vast swaths of data to deal with daily, having proper processes to organize and make sense of it is the only way to operate normally.
This is especially true for industries like Real Estate, where errors created through manual data entry can be detrimental to the entire organization. For this reason, most are turning to document automation technologies to help minimize these headaches, speed up the process, and eliminate costly errors altogether.
So, what are the top document automation trends for this year?
Currently, six new document automation trends in 2023 are set to change how businesses operate and interact with each other and their customers. These include; intelligent document analysis (IDA), named entity recognition, sentiment analysis, text similarity, text classification, and summarization.
We’ll be deep-diving into these in more detail, later in the article, right after we cover the five benefits leveraging document automation brings to businesses.
What are the benefits of document automation?
In the year 2023, we’re right in the middle of the digital era. That’s why smart businesses today are recognizing the many benefits that leveraging a document automation tool brings. From saving time to cutting costs, here are some benefits of leveraging document automation for your business.
Or, for our full breakdown of the benefits of document parsing software, check out this article.
Manual document processing is very time-consuming and costly for modern businesses.
Time equals money for companies today. So anything that helps to reduce time and streamline a business process (or more!) is worth its weight in gold. This is especially true for real estate firms, which often have to juggle multiple tasks throughout the day.
These daily tasks can include sorting through new and existing lead data, sending out messages, booking viewing appointments, document creation, and contract management, to name a few.
After these are completed, all of this generated and gathered data will need to be inputted accurately in the company CRM.
While realtors using manual data entry to organize and process their data may not have to worry about the costs of leveraging document automation software, they end up falling short in other areas.
For a start, they are at greater risk of data errors like misspelling their prospect’s names, misreading messages, missing viewings, or worse, making mistakes in contracts.
As you can imagine, this quickly cuts into revenue, costing them lost sales, company reputation, and even fines from legal proceedings. This affects a firm’s ability to scale up, putting them at a significant disadvantage against other agencies.
That’s why leveraging software that allows for document automation is imperative for firms who wish to cut costs and remain relevant to their customers.
To learn more about the best document process automation and document management and workflow automation tools currently on the market, check out our top real estate automation software article.
Reduces the time needed to create new documents
It’s no secret that although computers aren’t smarter than us (yet), they can complete tasks much faster and with greater accuracy than we do. This is especially true for repetitive tasks like document creation, particularly when locating, examining, and manually entering data from other existing documents to create new ones are involved.
A great example of this is with organizations like law firms which can save up to 82% of their time using a document automation tool to create contracts and other legal documents versus performing these tasks manually. As you can imagine, this allows them to get through caseloads much faster, leaving them much more free time to take on multiple cases simultaneously without compromising performance.
Minimizes human error
Human error creates a multitude of problems for businesses, including unnecessary delays from documents riddled with mistakes, extra man-hours, tax fines, and even a tarnished company reputation.
Even the simplest of errors like inputting a customer’s name incorrectly can make a brand appear like they don’t value their clients. This can harm a brand’s relationship with those customers, leaving a dent in a company’s reputation and making it more challenging to generate new business in the future.
For more serious mistakes in data-sensitive industries like the medical industry, companies can even risk being hit with severe fines and penalties or banned from doing business.
Boosts employee productivity
Every business seeks to save resources in whatever area it can. One of the most taxing resources for a company is man-hours spent on highly time-consuming tasks like manual data entry.
The surge of repetitive tasks in employees’ days puts pressure on them to split their attention between multiple tasks simultaneously, putting unnecessary strain on department budgets. This is detrimental to employee performance, and productivity as the human brain has evolved to focus on one task at a time, and simply lacks the architecture to perform two or more simultaneously.
By automating these processes for your workforce through the use of a document automation tool, you’ll help boost employee productivity by increasing manageability and visibility, all while significantly reducing human error.
With customer experience being at the forefront of every business model today, finding the most effective ways to delight them is the only way of retaining them in today’s hyper-competitive market.
IT departments, for example, are inundated with new technologies and processes that they forever need to keep up with. Through document automation, they can streamline their most time-consuming processes, get through support tickets 50% faster and see a 15% increase in the CSAT scores.
Similarly, marketing departments also benefit significantly from the power of automation.
Marketing automation tools allow companies to nurture their customers through automated emails and messages while collecting data from them through forms and interactions with their company. This all helps companies provide customers with more personalized interactions.
These customer experiences with brands are enhanced as they’re only sent relevant messages and offers rather than being bombarded with generic ones. This makes them more likely to want to interact with the brand and make more purchases with significantly less buyer remorse.
Check out our 11 marketing automation tips to learn how leveraging a marketing automation tool can help to delight your customers and streamline your business processes.
Top document automation trends to watch in 2023
Thanks to rapid technological advances, many new document automation trends are already changing the way modern companies process and organize their documents in 2023. Below are some of the most notable document automation trends for this year.
Intelligent document analysis
The most significant automation trend shifting how businesses operate today is intelligent document analysis or IDA.
Why? IDA allows businesses to convert unstructured and semi-structured data.
Unlocking these valuable insights from documents, IDA helps to make data usable in a fraction of the time using the power of natural language processing (NLP), a form of artificial intelligence.
Another massive benefit of an intelligent document automation tool such as IDA is that it creates enormous savings for businesses and much more significant returns on investment. As it leaves a digital trail, it also takes care of compliance, making it great for audit purposes.
This is invaluable for departments such as insurance, finance, data archive biotech & healthcare, and government agencies, to name a few.
Along with IDA, here are a few other trends currently changing the way businesses operate this year too:
Named entity recognition
Also referred to as entity chunking, named entity recognition (NER) automatically identifies key entities (pieces of information) in text like names, strings of words, and more using artificial intelligence.
It Does this in two steps: identifying a named entity (e.g., “The Great Barrier Reef,” which is composed of four tokens), then categorizing it (e.g., location, person, activity, etc.).
There are a variety of applications for NER across a range of industries. For example, HR departments use named entity recognition to identify candidates’ CVs and speed up the hiring process.
Search engines use NEP to increase their search tool’s relevancy when users type in a query, enabling them to find answers with increased accuracy – exactly why you’ll almost always find the answer on Google.
Hospital labs also benefit significantly from NEP, allowing them to extract essential patient data more quickly, greatly improving patient care, and even saving lives.
What if I told you that machines could actually identify and categorize human emotions in text?
Sentiment analysis (or opinion mining) does precisely that. Another tool using artificial intelligence, sentiment analysis can recognize opinions in text in numerous areas, including films, products, and companies, and if the expressed opinions are positive, negative or neutral.
This proves particularly useful for measuring customer success and marketing, where identifying customer opinions towards a brand on social media can provide valuable insights into current and future buyer behavior.
In doing so, these departments can see where they stand with their buyers, what’s working, and how they may need to adapt their current strategy. There are four main steps to sentiment analysis:
- Data collection
The first and possibly most crucial step in sentiment analysis is data collection. That’s because everything from this point onwards will rely heavily on the quality of the data collected here, how it’s been noted, and how it’s been categorized.
Many sentiment analysis tools use APIs to automatically pull information from News websites, social media, and even reviews from sites like Google and G2 Crowd for you. You can also manually upload information onto your sentiment analysis tool as a .csv file if you have it to hand.
- Data processing
After collecting data, sentiment analysis can process it depending on the format it extracted the data from.
For example, information from videos uses caption overlay to analyze any topic, entity, or aspect that you’ve labeled necessary. Speech from audio files like podcasts, for example, uses speech-to-text to ensure no essential data is missed.
Images in video or text use optical character recognition (OCR), whereas logos appearing in videos, including in the background on clothing, mugs, buildings, and more, use logo recognition, ensuring every detail is captured and processed.
For text, sentiment analysis will use word automation to process all text-based data, including hashtags and emojis, crucial for measuring opinions about your brand on social media channels.
- Data analysis
The third step in sentiment analysis is to analyze the data. For this to be achieved, the sentiment analysis needs to go through several stages, including:
- Training the model – This will require a pre-processed and manually labeled data set.
- Multilingual data – If more than one language is being analyzed, a dataset must be individually annotated and trained.
- Custom tags – Your model will need to be trained with custom tags and themes like the brand name to automatically isolate and extract important information from the analyzed text.
- Topic classification – This attaches categories like “vehicles” to your analysis in text like “I loved speed – the power of the horsepower of the engine is unparalleled.”
- Sentiment score – Each aspect or theme isolated is given either a positive, neutral, or negative score which is collected and calculated for the audience’s overall sentiment towards the brand.
- Data Visualization
This final step in sentiment analysis involves gathering the data collected from the steps above and creating an actionable report with them, usually in graphs or charts. This allows businesses to visually see the scores of each section individually and collectively, allowing for a better understanding of the areas they’re excelling in and which need improvement.
Text similarity uses natural language processing to identify similarities in text including sentences, paragraphs, and even whole documents. Included in this are entities, keywords, and even expressed topic representations. To analyze text effectively, text similarity uses a mathematical metric called cosine similarity.
Text similarity is helpful for several departments like academia where it can help teachers detect plagiarism in essays, and HR personnel match candidates to suitable roles based on crucial factors like skills and job titles.
This helps save these departments a lot of time compared to manually analyzing each document and means that important information like plagiarism percentages and candidate suitability are correctly identified.
Used to assign text items to one or multiple categories based on content, text classification has two measurements: The number of classes and the number of labels.
The number of classes simply means the binary classification of an item in which only one of two can be selected, for example, an email spam filter.
The number of labels text allows this data to be organized into only one class or multiple classes. For example, multiple-label classification might involve classifying books into several genre groups.
As the name suggests, summarization serves to generate a summary of main points using one of two critical approaches:
Extraction-based summarization simply generates a summary from a main body of text using its most important sentences. It does this without altering the original text.
Abstraction-based summarization is much more complex and still very experimental compared to extraction-based summarization. It uses artificial intelligence to condense the document by paraphrasing.
Both of these techniques allow readers to quickly and easily understand what messages are being communicated in text without having to spend time reading through an entire document.
Scientific journals and news feeds are great examples of this, allowing readers to quickly scan through the summaries of multiple documents in a fraction of the time.
Generate Quality Leads And Leverage Automation With Parserr
With the number of documents needing processing continually increasing and accuracy and customer-centricity being at the forefront of business today, the need for leveraging tools to handle this voluminous data is only set to grow.
Today, businesses are receiving more data than ever, with email being the most prevalent channel used to send and receive documents throughout the day.
On average, office workers receive around 121 emails daily – that’s a lot of documents to read, organize and extract. To remain relevant in today’s hyper-competitive market means organizations must find new and ingenious ways to accurately extract, manage and store this data.
For smart businesses looking for an effective way to manage these enormous volumes of data, a document automation solution – like Parserr – may just be the answer.
Specializing in parsing documents from emails, our software is quick and easy to install, extracting essential data in mere minutes, and saving you precious man-hours. In short, it lets you focus on the more critical elements of business, like product launches, ROI, campaigns, and customer success.
Whether you’re a sole trader looking to pull data from a few files at a time, or a large corporation needing to extract thousands, we have a range of plans to suit your business needs.
Ready to cut costs and rocket your business to the next level?
Contact Parserr today and try us FREE to be up and parsing in just 5 minutes!